BEGIN:VCALENDAR VERSION:2.0 PRODID:-//DDSA - ECPv6.18.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:DDSA X-ORIGINAL-URL:https://ddsa.dk X-WR-CALDESC:Events for DDSA REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Copenhagen BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20271031T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20260915 DTEND;VALUE=DATE:20260916 DTSTAMP:20260728T114736Z CREATED:20260728T114736Z LAST-MODIFIED:20260728T114736Z UID:10001983-1789430400-1789516799@ddsa.dk SUMMARY:Responsible Conduct of Research 2: Getting Ready for Submission of Manuscripts and Thesis DESCRIPTION:Enrolment guidelines \nJOURNALEN: ALLE SKAL PÅ VENTELISTEN\, OG I BEHØVER IKKE TJEKKE. PAMELA OG JESPER SORTER\, TJEKKER OG TILMELDER LØBENDE \n \n”Special rules apply for this course”\n \nThis course is ONLY for PhD students enrolled at SCIENCE in the second or last year of their PhD Plan. All other applicants will get a rejection. \nWhen submitting a paper or a PhD thesis one is typically confronted with a great deal of responsible conduct of research issues. These are issues on potential plagiarism\, open access requirements\, data management\, authorship issues\, documentation of ethical and legal permissions\, declaring one’s conflicts of interest\, etc. It is not always easy to navigate in these issues\, covering a web of different institutions’ expectations\, legal requirements\, and societal and academic norms. Many PhD students are therefore taken by surprise by these matters on the night before deadline. In this course we therefore guide the PhD student through the most general aspects of these issues as they appear in the process of submitting a paper or a PhD thesis. Furthermore\, participation in the course gives access to a pre-screening of the PhD thesis for potential plagiarism prior to submission. \nCourse purposeHelping the PhD student to submit\, responsibly and successfully\, the PhD student’s thesis as well as papers to scientific journals. \nCourse targetThe course is open to all PhD students at the Faculty of SCIENCE\, University of Copenhagen\, and is mandatory for all PhD students enrolled after 1 August 2020 University of Copenhagen. The course is intended for PhD students who expect to submit their PhD thesis within 12 months. \nLearning objectives– Understand what it takes to stay clear of plagiarism and selfplagiarism. – Understand and interpret the result of iThenticate’s screening for duplicate text.– Understand and apply relevant legal\, scientific and societal norms (of e.g. but not only Responsi-ble Conduct of Research) in regards to the following specific subjects:* Plagiarism and selfplagiarism * Open access * Authorship* Conflicts of interests* Ethical and legal permissions* Data management \nRequirements and expectationsPrior to the course the PhD student must have some draft of a paper intended to be submitted to a scientific journal\, or (if the PhD student is writing a monography) some draft of a dissertation. The course is intended for PhD students who expect to submit their PhD thesis within 12 months. We recommend that the PhD student has passed the mandatory RCR1 course prior to this course. \nFormat of the course4 hours course with teacher presentations and exercises directly aligned with the mandatory as-signment. \nContent of the course– (Self-)plagiarism issues – Interpretation of iThenticate results– Open Access issues– Authorship issues– Conflicts of interest issues– Issues relating to ethical permissions– Data Management issues– Other issues \nAssignmentThe PhD student is given a list of challenges to guide the PhD student’s submission(s) of papers and thesis. The PhD student’s assignment is to fill out a logbook following the list and\, for each checkpoint\, explain how the PhD student is dealing with the challenge\, or how the PhD student intends to deal with the challenge. One week after the course the PhD student submits the log-book to the teacher of the course. Two weeks after submission of the logbook\, the PhD student receives a notification of the logbook being approved or not approved. If not approved the PhD student will receive an explanation and be asked to revise and re-submit within two weeks. All exchange of logbooks happens via Absalon and all parts of this assignment are mandatory.The PhD student has three attempts to pass the assignment. If the assignment is not passed after three attempts\, the PhD student must take the course again. Failure to meet a deadline results in a failed attempt. \nECTSParticipation in this course will be granted 1 ECTS. \nLimitationEach course has no more than 25 participating PhD students. The course is offered 5-6 times each year. \nCourse Director and locationAssociate Professor Mads Paludan Godskesen\, Department of Food Resources and Economics \nThe course takes place at Frederiksberg Campus or ONLINE from 12:00 to 16:00 \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/responsible-conduct-of-research-2-getting-ready-for-submission-of-manuscripts-and-thesis/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260824 DTEND;VALUE=DATE:20260829 DTSTAMP:20260915T103003Z CREATED:20260915T103003Z LAST-MODIFIED:20260915T103003Z UID:10001898-1787529600-1787961599@ddsa.dk SUMMARY:Small-angle scattering: Principles\, Data Analysis and Advanced Modeling DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and ContentThis 2.5 ECTS course introduces small-angle scattering using X-rays (SAXS) and neutrons (SANS) for structural and dynamic investigations. The course will have a strong hands-on approach\, combining lectures\, simulations\, walked-through analysis examples and actual experiments. For the latter\, we will use the three SAXS machines at University of Copenhagen. Passing the course requires active participation\, presentations during the course and submission of a final report. The course is relevant for all fields of material science where structure on the nanoscale is important and for students aiming to use SAS in their project\, for example polymer science\, food science\, plant science\, nanoparticle research\, soft matter physics\, structural biology\, drug-delivery etc. \nTentative program / list of modules:Preparations before the course (to be finalized)• Book chapters/review papers (will be announced on the Absalon course site)• Prepare a slide about your own project (upload to Absalon course site)• Quiz/online tutorial (will be announced on the Absalon course site) \nIntroduction to the principles of x-ray and neutron scattering (Monday)• Properties of x-rays and neutrons• Form factors\, structure factors\, polydispersity\, scattering contrast and variation thereof \nExperimental principles (Monday)• Lab source vs synchrotron radiation setups• Sample preparation • Advanced sample environments• In situ and time-resolved measurements• USAXS/USANS setups \nExperiments (Monday)• Experiments at (one of) UCPH’s three SAXS instruments• Calibration samples for SAXS/WAXS • Absolute intensity calibration• Prepare and measure student’s own samples \nBasic modeling and analysis of scattering data (Tuesday)• Monte Carlo simulation of scattering patterns• Model fitting in SasView\, \nPrimary data reduction and analysis (Tuesday)• Data reduction and background subtraction• Data quality assessment • Primary data analysis\, including Guinier analysis\, Porod and Kratky plots\, and indirect Fourier transformation (IFT). \nAnalysis of data from hierarchical structures (Wednesday)• The relation between slopes on log-log plots and dimensionality and surface roughness• Fitting using generic hierarchical models and physical models with several structural levels. \nAdvanced modelling (vary from year to year and will depend on the availability of relevant Guest lectures) • Reparametrization and molecular constraints• Anisotropic data analysis• Integrating SAS with molecular dynamics simulations• Integration of Alpha-fold into modelling of SAS data \nSpecialized applications (vary from year to year and will depend on the availability of relevant Guest lectures)• In the August 2024 edition\, we had a module on GISAXS in this category\, and in the 2025 edition we had a module on SESANS. Possible topics for the 2026 course: USAXS/USANS\, or time-resolved SAXS\, or SAXS at XFEL \nLearning outcomesKnowledge Through lectures and theoretical exercises\, the students will:• Understand the principles of how structural information can be obtained from X-ray or neutron scattering. This including knowledge of central concepts such as form factors and structure factors.• Be able to explain the experimental setup for a SAXS/SANS experiment\, including important corrections.• Be aware of possibilities and limitations of different strategies for analysing the data. \nSkills Through hands-on exercises and tutorials the students will be able to:• Reduce and quality check their data\, including error analysis and aggregation state.• Perform primary data analysis\, including Guinier analysis and indirect Fourier Transformation.• Analyse their data with relevant software\, including software packages SasView and ATSAS. \nCompetences Through reflection and discussions of practical and theoretical aspects\, the students can:• Design and prepare a relevant SAXS or SANS experiment to answer a given scientific question.• Decide on a relevant analysis strategy and do the analysis. \nTarget GroupPhD students and postdocs working with soft matter. This spans colloids or exotic lipid phases in food science\, drug delivery particles and protein/nucleotide complexes in pharmacy\, and new polymer-based materials in organic chemistry. \nRecommended Academic QualificationsA MSc from science or health. We have students from a broad background of natural sciences and health. Therefore\, we aim to design the course to make it accessible with all these backgrounds. To achieve this\, we have much emphasis of hands-on-work and work on individual projects. \nResearch AreaFood science\, pharmacy\, material science. \nTeaching and Learning MethodsWe apply a modular concept\, with modules of 1.5-2 hours. Each module should contain an introduction to the subject\, hands on exercises\, and be followed up by institutionalization. We follow this structure ourselves\, and also help our guest lectures to use this format. To support a hands-on teaching\, we have developed an online learning platform sastutorials.org. Our teaching philosophy has been described more thoroughly in Larsen et al 2025\, J. Appl. Cryst 58: 603-608 (https://doi.org/10.1107/S1600576725001062) \nType of AssessmentWe require the students to participate actively\, and to present their project work orally on the last day of the course and in a written report just after the course. This is required to pass the course\, but we do not provide any grading. These presentations receive feedback from us. \nLiteratureTo be announced \nCourse coordinatorJacob Kirkensgaard \nGuest LecturersAnnette Langkilde\, University of Copenhagen\, Department of Drug design and PharmacologyJan Skov Pedersen\, Aarhus UniversityWim Bouwmann\, Delft University of TechnologyMore international guest lecturers – To be confirmed \nDates24-28 August 2026 \nExpected frequencyAnnually in mid to late August \nCourse locationTo be announced \nCourse fee• Participant fee: 0 DKK• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 3.000 DKK• PhD student from foreign university: 3.000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 3.000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 3.000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 8.400 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/small-angle-scattering-principles-data-analysis-and-advanced-modeling/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260824 DTEND;VALUE=DATE:20260829 DTSTAMP:20260728T114738Z CREATED:20260728T114738Z LAST-MODIFIED:20260728T114738Z UID:10001786-1787529600-1787961599@ddsa.dk SUMMARY:Advanced measurements & analyses of GHG fluxes from soils & ecosystems (AMAGS) DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved to other applicants. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and ContentAim: The course will focus on teaching the students how to measure and calculate the exchange of GHG’s between the soil/ecosystem and the atmosphere using state-of-the-art chamber and analyzer technologies. The course will highlight the conceptual\, technological and analytic challenges involved in obtaining the “true” measure of the GHG flux between the soil and the atmosphere and how these data can be used to address fundamental knowledge gaps related to biogeochemical feedbacks to current and future climate. \nBackground: The chamber method is the most widely used for GHG flux measurements\, but comes with the downside of being extremely time consuming. However\, recent development in combining novel chamber design with real-time GHG analyses now allows for automation of the flux measurements using very short time scales\, compared to earlier technologies. These advances now rival the temporal resolution of eddy covariance measurements often considered as the golden standard in GHG flux measurements.This development represent a significant advantage as it is known that long measurement times using chambers severely biases the flux measurements meaning that flux quality is greatly improved. Another advantage of automated chambers is the potential to resolve spatiotemporal patterns in much higher detail than possible with other GHG flux techniques. Because chambers target small areas they can be deployed to measure spatial patterns which are highly needed to understand soil physical\, chemical and biological drivers of GHG fluxes. \nContent: The course will be based on field work done at our state-of-the-art GHG facilities\, Brandbjerg and Højbakkegård\, where the students will be introduced to and work with highly advanced technologies to quantify exchange of greenhouse gases in agriculture\, grassland/heatland and forest ecosystems (i.e. 15 h of practical exercises over two days in the field\, KSL\, JRC\, JP\, SB\, AB). Automatic chamber systems in operation at the sites allow students to get an insight in to real research projects dealing with GHG exchange and will work as a basis for class room discussion and learning. Another 15 hours will be spent working with the data obtained in the field (i.e. 15 h theoretical exercises). Remaining time during the course will be spent on lectures by RK\, JP\, KSL\, JRC\, and AB (5 h) and class instructions for the theoretical exercises and summaries by RK\, JP\, KSL\, and JRC (5 h). The 30 hours of preparation time will be spent on reading the suggested reading and an e-learning pre-assignment (10 h\, KSL and JRC) to familiarize themselves with the R software and two specific R packages typically used for calculating the GHG fluxes\, i.e. the HMR package (co-developed by JP) and the goFlux package (co-developed by KSL and JRC). \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge• describe commonly used chamber methods and equipment for measuring greenhouse gas fluxes from soils/ecosystems/water surfaces• demonstrate the field use of the chamber method with different gas analyzers• discuss theory of sampling design \nSkills• work independently with the chamber methods under field conditions • evaluate the pros and cons of using specific designs to measure greenhouse gas fluxes• apply the sampling methodology in the field• design a problem-oriented scientific field sampling protocol for greenhouse gas fluxes \nCompetences• project-oriented group work in the field• choose the correct techniques to obtain a representative flux of greenhouse gases• analyze field data using graphic and statistical techniques in R• synthesize results in a written report \nTarget GroupThe course target group is PhD students\, who wants to learn about technologies and data analytical tools for measuring and calculating the exchange of one or more greenhouse gases between soils / ecosystems and the atmosphere. The course is relevant to many PhD students studying various aspects of plant-microbe processes\, interactions\, and responses to changes in land use management\, pollution\, and climate across multiple ecosystem types spanning from intensive agricultural systems to more complex natural ecosystems. \nRecommended Academic QualificationsThe PhD student should be working with some aspect of greenhouse gas exchange between soil/water surface/ecosystem and the atmosphere in their PhD project. A master’s degree with previous experience is an advantage but not a requirement. \nResearch AreaIt is critical for environmental scientists to quantify the major sources and sinks of the most common greenhouse gases (GHG) as well as to disentangle the processes involved in GHG exchange in terrestrial ecosystems including streams\, rivers and lakes. Such data and knowledge are essential for the development of national and international strategies for sustainably managing energy production and land use. \nTeaching and Learning MethodsAMAGS focuses on hands-on experience for the students by measuring the exchange of GHG’s between the soil and the atmosphere using the chamber methodology. It is a key element of the course that participant will perform fieldwork testing the theoretical basis of the course at a real field site under the guidance of the course teachers. Furthermore\, it is central to the course that collected data are integrated into the theoretical exercise part of the course (i.e. 15 h in total). With this emphasis on doing science the course will highlight the conceptual\, technological and analytic challenges involved in obtaining the “true” measure of the GHG flux between the soil/ecosystem and the atmosphere.Key concepts of the course will be presented by keynote lectures by RK\, JP\, AB\, JRC and KSL\, as well as discussed on the background of presentations by the students\, group work and fieldwork. The course starts with establishing a knowledge base by reviewing current literature within the research field prior to course start. This will form the basis for an active involvement of PhD students in the specific theoretical and methodological problems\, how to construct a research question and carry out a field sampling design with hands-on experiments and evaluate the data collection techniques through actual analyses of field data. The course is finalized by group presentation by students and a written report submitted after the course presenting and discussing the collected data and results. The proposed PhD course expands the scope of our collaboration with RK by taking advantage of his unique competences in biogeochemistry and GHG measurements with his expertise as a teacher. \nType of AssessmentParticipants must hand in a written report summarizing and discussing the results of data obtained and analysed during the course \nLiteratureManual chambers:Christiansen\, J. R.\, Korhonen\, J. F. J.\, Juszczak\, R.\, Giebels\, M.\, & Pihlatie\, M. (2011). Assessing the effects of chamber placement\, manual sampling and headspace mixing on CH4 fluxes in a laboratory experiment. Plant and Soil\, 343(1–2)\, 171–185. https://doi.org/10.1007/s11104-010-0701-y.Pihlatie\, M. K.\, et al. (2013). Comparison of static chambers to measure CH4 emissions from soils. Agricultural And Forest Meteorology 171-172: 124-136. 10.1016/j.agrformet.2012.11.008Xu\, L.\, Furtaw\, M. D.\, Madsen\, R. A.\, Garcia\, R. L.\, Anderson\, D. J.\, & McDermitt\, D. K. (2006). On maintaining pressure equilibrium between a soil CO2 flux chamber and the ambient air. Journal of Geophysical Research Atmospheres\, 111(8)\, 1–14. https://doi.org/10.1029/2005JD006435.Christiansen\, J. R.\, Outhwaite\, J.\, & Smukler\, S. M. (2015). Comparison of CO2\, CH4 and N2O soil-atmosphere exchange measured in static chambers with cavity ring-down spectroscopy and gas chromatography. Agricultural and Forest Meteorology\, 211–212\, 48–57. https://doi.org/10.1016/j.agrformet.2015.06.004.Thalasso\, F.\, Riquelme\, B.\, Gómez\, A.\, Mackenzie\, R.\, Aguirre\, F. J.\, Hoyos-Santillan\, J.\, Rozzi\, R.\, and Sepulveda-Jauregui\, A.: Technical note: Skirt chamber – an open dynamic method for the rapid and minimally intrusive measurement of greenhouse gas emissions from peatlands\, Biogeosciences\, 20\, 3737–3749\, https://doi.org/10.5194/bg-20-3737-2023\, 2023. \nAutomated chambers:Brændholt\, A.\, Larsen\, K.S.\, Ibrom\, A.\, and Pilegaard\, K.: Overestimation of closed-chamber soil CO2 effluxes at low atmospheric turbulence\, Biogeosciences\, 14\, 1603–1616\, https://doi.org/10.5194/bg-14-1603-2017\, 2017.Lee\, JS. Comparison of automatic and manual chamber methods for measuring soil respiration in a temperate broad-leaved forest. j ecology environ 42\, 32 (2018). https://doi.org/10.1186/s41610-018-0093-0.Flux calculation:Hutchinson\, G. L.\, & Mosier\, A. R. (1981). Improved Soil Cover Method for Field Measurement of Nitrous Oxide Fluxes. Soil Science Society of America Journal\, 45(2)\, 311. https://doi.org/10.2136/sssaj1981.03615995004500020017x.Pullens J.W.M.\, etal. (2023) Identifying criteria for greenhouse gas flux estimation with automatic and manual chambers: A case study for N2O. European journal of soil science\, 74:e13340. https://doi.org/10.1111/ejss.13340.Rheault et al. (2024). goFlux: A user-friendly way to calculate GHG fluxes yourself\, regardless of user experience. Journal of Open Source Software\, 9(96)\, 6393. https://doi.org/10.21105/joss.06393 (https://qepanna.quarto.pub/goflux/)Hüppi\, R.\, Felber\, R.\, Krauss\, M.\, Six\, J.\, Leifeld\, J.\, & Fuß\, R. (2018). Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates. PLOS ONE\, 13(7)\, e0200876. https://doi.org/10.1371/journal.pone.0200876.Chamber guideline papers (for reference):Pavelka M.\, et al (2018) Standardisation of chamber technique for CO2\, N2O and CH4 fluxes measurements from terrestrial ecosystems. International Agrophysics\, 32\, 569-587. doi: 10.1515/intag-2017-0045.Maier M.\, et al. (2022) Introduction of a guideline for measurements of greenhouse gas fluxes from soils using non-steady-state chambers. J. Plant Nutr. Soil Sci. 2022;185:447–461. doi: 10.1002/jpln.202200199. \nCourse coordinatorKlaus Steenberg Larsen (KSL)\, Associate Professor\, ksl@ign.ku.dkJesper Riis Christiansen (JRC)\, Associate Professor\, jrc@ign.ku.dk \nGuest LecturersRK (Senior scientist and head of division at Karlsruhe Institute of Technology\, IMK-IFU\, Germany) is an expert on GHG measurements (CO2\, CH4 and N2O) and feedback mechanisms of global environmental changes on terrestrial ecosystems. He has worked for >25 years with measuring and modelling C and N turnover and associated matter fluxes in natural and managed ecosystems at site and landscape scale. RK contributes to the course with lectures and instructions during theoretical exercises. \nJP (Tenure Track Assistant Professor\, Dept. of agroecology\, Aarhus University) is an expert on eddy co-variance measurements of GHG exchange as well as on the calculations of fluxes using R software and flux calculation packages. In particular\, an expert on the HMR package\, where he was a co-developer of the latest version. JP contributes to the course with lectures and instructions during theoretical exercises as well as partially in the field. \nThe proposed PhD course expands the scope of our collaboration with both RK and JP by taking advantage of their unique knowledge in the field of biogeochemical cycling of ecosystems and GHG measurement expertise. \nDates24 – 28 August 2026 \nCourse locationKU-IGN\, Rolighedsvej 23\, 1958 Frederiksberg C – and at field sites in Jægerspris and at Højbakkegård. \nCourse fee• Participant fee: 1000 DKK (All participants)• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 3000 DKK• PhD student from foreign university: 3000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 3000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/advanced-measurements-analyses-of-ghg-fluxes-from-soils-ecosystems-amags/ LOCATION:Department of Geoscience and Natural Resource Management CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260824 DTEND;VALUE=DATE:20260829 DTSTAMP:20260728T114738Z CREATED:20260728T114738Z LAST-MODIFIED:20260728T114738Z UID:10001696-1787529600-1787961599@ddsa.dk SUMMARY:Fundamentals of the PhD education at SCIENCE - module 2 - K11 DESCRIPTION:Enrolment guidelines \nThe module is mandatory for all new PhD students enrolled at Science from 1 January 2024 as part of the course ‘Fundamentals of the PhD education at SCIENCE’. \nIf you are a double degree student with a foreign host\, you can not enroll in this course. \nLearning objectivesThe learning outcomes for the segments are given below. Each outcome is marked K for Knowledge\, S for Skills\, or C for Competences. \nData Management segment:• Identify relevant legislation\, requirements\, and policies on data management applicable to research projects at UCPH (K).• Recognize recommendations and requirements regarding open and reproducible research designs\, data collection\, and data publication (K).• Classify data and conduct a risk assessment to ensure the secure storage of data (S).• Assess how data can be preserved and shared to guarantee FAIR use of data (S).• Assess when to use electronic lab notes (S).• Contribute to planning and conducting appropriate data and materials management in all phases of their PhD project (C). • Be able to adhere to best practices of open and reproducible research (C).Career Management segment:• Differentiate typical career paths for SCIENCE PhDs (K).• Describe selected understandings of motivation (K). • Explore and explain their career priorities (S).• Build a professional profile on LinkedIn (S).• Assess how different uses of their change of scientific environment may impact their career (S).• Integrate knowledge of typical career paths for SCIENCE PhDs with an understanding of their personal career priorities (C).• Develop a personal networking strategy and build a professional network that supports their career interests (C).Data Science segment:• Know Data Science as a research methodology (K). • Apply basic Statistics or Machine Learning computational frameworks and methods when appropriate in their research (S).• Build a network for potential inter-disciplinary data science collaborations (C). \nContentThe purpose of the course is to introduce the students into Data Science\, Data Management\, and Career Management:• Many students will apply/develop Data Science methods (data analysis\, statistics\, and machine learning) directly in their own research; and all students should be aware of this potential. • Some students will directly apply Data Management principles\, and all students should be aware of the general policies.• All students should actively manage their careers. \nThe module consists of 10 morning/afternoon sessions during the on-campus course week (2 on Data Management\, 1 on Career Management\, and 7 on either Machine Learning or Statistics). Prior to course start\, the students will choose to follow either the Statistics or Machine Learning variant of the module. \nThe aim of the Data Management segment is to equip PhD students with knowledge and skills to:• Manage data and primary materials responsibly during their PhD projects.• Create open and reproducible research outputs. \nThe aim of the Career Management segment is that PhD students start to explore their values\, motivation\, and the great variety of career options that are open to them after their PhD. The segment will cover:• Megatrends in the labour market and typical career paths for PhDs from the natural sciences.• Motivation\, values\, and career priorities.• Change of scientific environment.• Networking for career development. \nThe Data Science segment will aim ensure that PhD students will consider Data Science as a methodology and allow them to apply basic Statistics or Machine Learning methods when appropriate in their research. \nThe Statistics variant will cover:• Statistical thinking and methodology\, including statistical power and principles for experimental design.• Statistical modelling using fixed and random effects.• Tabular and graphical presentation of experimental results and statistical analyses.• The R programming environment for Statistics and basic comparisons to other statistical software. \nThe Machine Learning variant will cover:• Machine learning foundations and methods.• Data Science caveats and best practice.• Introduction into AI with focus on intuitive understanding the big models in terms of potential\, applicability\, limitations\, and sustainability.• Application of AI and machine learning in science. \nParticipantsAll PhD students at SCIENCE as part of the PhD Fundamentals course. Module 2 is about 6 months into the PhD programme. \nLanguageEnglish \nFormThe module will include a mixture of lectures\, exercises\, plenary and group discussions\, and peer feedback during the on-campus segments. Around the on-campus week\, the students will perform eLearning as well as reflective and practical assignments. \n Type of assessmentAll segments require presence and active participation.The students must hand in a report where they choose between a Data Management Plan\, or a report related to their Statistics or Machine Learning specialization. \nRegistrationAutomatic registration when enrolling in the PhD program at SCIENCE. \nRemarksThe PhD School at the Faculty of SCIENCE is committed to building a learning environment that welcomes\, includes\, and empowers all its PhD students. By building a Faculty-wide peer community of PhD-students with the Fundamentals course\, we secure that all PhD candidates are given adequate instruction in a range of essential competences that lie outside the core scientific research skills offered through supervision\, tool-box and specialized PhD courses. Moreover\, we build bridges between different research programmes at the Faculty of SCIENCE and offer diverse\, multidisciplinary fora of exchange strengthening the PhD candidates’ scientific and social networks and laying the foundation for a strong alumni culture. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/fundamentals-of-the-phd-education-at-science-module-2-k11/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260810 DTEND;VALUE=DATE:20260822 DTSTAMP:20260915T103005Z CREATED:20260915T103005Z LAST-MODIFIED:20260915T103005Z UID:10001979-1786320000-1787356799@ddsa.dk SUMMARY:Microbiome analysis using 2nd and 3rd generation DNA sequencing DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and ContentAdequate generation and analysis of DNA sequencing data are essential skills for scientists across medicine\, research\, and industry. This course provides theoretical and practical training in microbiome analysis using 2nd (Illumina) and 3rd (Oxford Nanopore Technologies\, ONT) generation sequencing technologies\, with particular emphasis on nanopore technology. \nParticipants will engage in hands-on sample preparation\, DNA sequencing\, and analysis of raw data (e.g.\, pod5/fastq files). They will learn to map amplicons (e.g.\, 16S rRNA gene)\, reconstruct genomes\, and estimate abundance tables (e.g.\, OTU/ASVtables) using state-of-the-art\, open-access bioinformatics tools. Exploratory analysis (e.g.\, alpha- and beta-diversity) and advanced statistical approaches for microbiome data will also be covered\, including tools for linking microbiome profiles to other omics and environmental data. \nThe course is designed for PhD students and industry representatives and spans 10 days (2 weeks\, Monday to Friday). The first week emphasizes wet lab techniques\, including the generation and initial processing of raw sequencing data. The second week focuses on the computational analysis of microbiome data\, including the datasets generated during the course. \nLearning outcomes \nIntended learning outcome for the students who complete the course:• Assess DNA quality and concentration using spectrophotometric and colorimetric methods.• Prepare barcoded 16S rRNA gene amplicon libraries for Illumina and ONT sequencing.• Generate sequencing libraries for near full-length 16S rRNA genes and full genomes using ONT.• Load and operate sequencing platforms (e.g.\, MinION\, PromethION\, flongle\, Illumina iSeq).• Analyze raw sequencing data (Illumina fastq and ONT pod5) to generate OTU tables using R based tools.• Perform alpha- and beta-diversity analyses and exploratory statistics for microbiome profiling.• Conduct discriminant analysis (DA) and differential abundance testing using advanced statistical tools. \nKnowledge:By the end of the course\, participants will have knowledge about:• Principles of 2nd and 3rd generation DNA sequencing technologies (Illumina\, Oxford Nanopore).• Strengths\, limitations\, and applications of sequencing platforms for microbiome analysis.• Theoretical background of microbial community profiling\, genome reconstruction\, and amplicon-based approaches.• Statistical approaches for microbiome data\, including alpha-/beta-diversity\, discriminant analysis\, and differential abundance testing.• Integration of microbiome data with other omics and environmental datasets. \nSkills:Participants will be able to:• Assess DNA quality and concentration using spectrophotometric and colorimetric methods.• Prepare barcoded 16S rRNA amplicon libraries for Illumina and ONT sequencing.• Generate sequencing libraries for near full-length 16S rRNA genes and full genomes using ONT.• Operate sequencing instruments (MinION\, PromethION\, flongle\, Illumina iSeq).• Analyze raw sequencing data (Illumina fastq\, ONT pod5) to produce OTU/ASV tables using R-based tools.• Perform exploratory and advanced statistical analyses on microbiome data. \nCompetences:Upon completion\, participants will be competent to:• Design and execute microbiome sequencing experiments from sample preparation to data analysis.• Critically evaluate sequencing data quality and analytical pipelines.• Apply appropriate bioinformatics and statistical methods to their own PhD or professional projects.• Integrate microbiome data with other biological and environmental datasets to draw meaningful scientific conclusions.• Communicate methods\, results\, and interpretations in a written scientific report. \nTarget GroupThe course is primarily aimed at PhD students working with microbiome research\, DNA sequencing\, and related omics technologies. It is also highly relevant for laboratory technicians and specialists from private companies and industry who are engaged in microbiome studies\, sequencing-based workflows\, or bioinformatics data analysis. \nRecommended Academic QualificationsParticipants are expected to have a background in life sciences (e.g.\, microbiology\, molecular biology\, biotechnology\, bioinformatics\, food science\, or related disciplines) and an interest in applying state-of-the-art sequencing technologies and computational tools to microbiome research. \nResearch AreaThe course is relevant to research areas within microbiology\, molecular biology\, biotechnology\, bioinformatics\, food science\, and environmental science. In particular\, it targets research focusing on microbiome analysis\, DNA sequencing technologies\, microbial ecology\, and multi-omics integration across both academic and industrial contexts. \nTeaching and Learning MethodsThe course will combine theoretical teaching and practical training to ensure participants gain both conceptual understanding and hands-on experience. Instruction will include:• Lectures (25 hours): Introducing principles of 2nd and 3rd generation sequencing technologies\, microbiome analysis\, and statistical approaches.• Laboratory Sessions (20 hours): Hands-on training in DNA extraction\, library preparation\, and operation of Illumina and Oxford Nanopore sequencing platforms.• Theoretical Exercises (30 hours): Guided computational work on microbiome datasets\, covering raw data processing\, OTU/ASV table generation\, and advanced statistical analyses using R-based tools.• Course Preparation (15 hours): Pre-reading and familiarization with course material.• Report Writing (47.5 hours): Independent work where participants analyze data (optionally including their own PhD project datasets) and prepare a written report. \nThis blended format ensures alignment between theory and practice\, reflecting the workload distribution specified for the 5.5 ECTS course. \nType of Assessment• Write and document a dedicated analysis script (e.g.\, in R or bash) for a specific dataset type\, such as raw sequencing data (Illumina fastq or ONT pod5 files) or processed feature/abundance tables.• Include both the code (lines of script) and comments explaining the analysis steps to ensure reproducibility and clarity.• Perform analyses such as OTU/ASV table generation\, alpha-/beta-diversity estimation\, or advanced statistical testing.• Interpret and discuss the results\, with the option to integrate data from their own PhD project or professional context.• Assessment will be conducted on a pass/fail basis\, determined by the quality and completeness of the submitted script and report\, as well as active course participation. \nCourse coordinatorAssociate professor Lukasz Krych \nGuest Lecturers1. Pablo Atienza LopezBioinformatician\, Genomics of Gene Expression Lab\, ValenciaTopic: Genome assembly\, annotations\, and raw data treatment2. Farhad M. Panah\, PhDUnseen Bio\, DenmarkTopic: 16S rRNA gene amplicon sequencing data treatment3. Witold Kot \, PhDTenure Track Assistant Professor\, Department of Plant and Environmental Sciences\, University of CopenhagenTopic: Illumina sequencing lectures \nDates10-08-2026 to 21-08-2026 \nExpected frequencyThe course is offered annually. The 2026 edition will mark the 8th edition of the course\, and it is expected to continue in the coming years with further development and refinement of content and teaching methods. \nCourse locationThe course will take place at the Department of Food Science\, Faculty of Science\, University of Copenhagen. \nDeadline for registrationThe deadline for registration is 27 July 2026.Applications received after this date will only be considered if seats remain available.Registration is binding\, and available seats are limited to 60 participants. \nCourse fee• Participant fee: 1.000 DKK• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 6.000 DKK• PhD student from foreign university: 6.000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 6.000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 6.000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 16.800 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/microbiome-analysis-using-2nd-and-3rd-generation-dna-sequencing/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260810 DTEND;VALUE=DATE:20260815 DTSTAMP:20260915T103004Z CREATED:20260915T103004Z LAST-MODIFIED:20260915T103004Z UID:10001895-1786320000-1786751999@ddsa.dk SUMMARY:Quantitative Sustainability Assessment DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved for PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules.Anyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and ContentThe course is designed to equip students with the necessary tools to reflect upon and quantify sustainability performance\, a crucial component in research\, policymaking\, corporate decision making\, and reporting. It focuses on introducing the fundamental principles of quantitative sustainability assessment of various production systems\, including dynamic carbon modelling and utilizing unexplored side streams along value chains. Key methodological approaches such as material flow analysis\, input-output modelling\, life cycle assessment\, and carbon dynamics will be covered\, with an emphasis on defining functional units\, time scales\, and system boundaries. This practical application will be facilitated through individual or group problem-oriented projects\, employing a variety of analytical tools and methods chosen by the students. Throughout the course\, critical topics such as resource and emission capture\, zero waste strategies\, and cascade utilization within circular business ecosystems will be explored through interactive lectures and practical applications. This approach fosters an environment of active learning\, allowing participants to directly engage with the material and apply what they learn to real-world scenarios. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge:• Understand the principles\, requirements\, and limitations behind quantitative sustainability assessment methods such as material flow analysis\, life cycle assessment (LCA)\, input-output modeling\, and dynamic carbon modeling. • Grasp the importance of selecting meaningful parameters when defining system boundaries\, functional units\, and allocation methods. • Recognize the role of unexplored sidestreams in enhancing sustainability within circular business ecosystems. \nSkills:• Apply various quantitative sustainability assessment tools and methods to their own data sets\, enhancing their ability to analyze and predict environmental impacts. • Select appropriate tools and methodologies freely\, including selected software or Excel\, to address specific research questions in process optimisation\, business or policy implications. • Based on the aim of the assessment\, discuss\, identify and rank which factors should be included in a sustainability assessment. • Be able to engage in the public debate about the sustainability of transitions. \nCompetences:• Apply various quantitative sustainability assessment tools and methods to their own data sets\, enhancing their ability to analyze and predict environmental impacts. • Select appropriate tools and methodologies freely\, including selected software or Excel\, to address specific research questions in process optimisation\, business or policy implications. • Based on the aim of the assessment\, discuss\, identify and rank which factors should be included in a sustainability assessment. • Be able to engage in the public debate about the sustainability of transitions. \nTarget GroupThis course is designed for PhD students engaged in various sectors of the bioeconomy\, particularly those from forestry\, food science\, agriculture\, biotechnology\, and environmental science. It is ideal for individuals interested in sustainable innovation in resource management and technologies within circular economy implementations across these critical areas. Emphasizing resource and emission capture and utilization\, zero waste practices\, and cascade utilization strategies\, the course prepares students to develop and implement circular business models that optimize resource use and minimize environmental impact. The course aims to attract a diverse group of students\, including those in continuing education programs\, who are eager to apply circular economy principles to enhance sustainability in the forest and food sectors\, as well as other related bioeconomic fields. This interdisciplinary approach ensures that participants from different backgrounds can contribute to and benefit from the course\, fostering a comprehensive understanding of circular bioeconomy and promoting practices that lead to more sustainable and resilient bioeconomic sectors. \nRecommended Academic QualificationsParticipants should have a Master’s degree or equivalent. Proficiency in data analysis with tools such as R\, Python\, MATLAB\, SAS\, or Excel is beneficial. A foundational understanding of life cycle thinking or knowledge of LCA is crucial. This background will enable effective engagement with the course’s focus on sustainable resource management and environmental evaluations. \nResearch AreaSustainability \nTeaching and Learning MethodsThe course is conducted through various teaching and learning methods: \nLectures: To present the fundamentals and applications of quantitative sustainability methods\, such as dynamic carbon modeling and life cycle assessment\, within the context of circular economy principles. Exercises: To train students in applying these methods using a variety of tools\, enhancing their practical skills in sustainability analysis. Case Work: Students are encouraged to bring their own data sets for problem-oriented projects\, applying the learned concepts to real-world scenarios either individually or in collaborative groups. Discussions and Reflections: Regular sessions are held to discuss the opportunities and limitations of sustainability assessment\, fostering a critical understanding of resource management and systems design within various bioeconomic sectors. \nType of AssessmentCourse participation is assessed based on an individually prepared essay/presentation outlining the role of sustainability assessment in their own PhD project and how it could be assessed\, including pros and cons of the chosen method(s). \nLiteratureReading material will consist of a number of scientific papers and/or book chapters which will be made available to students prior to the course. \nCourse coordinatorNiclas Scott Bentsen\, IGN. Marianne Thomsen\, FOOD. \nGuest LecturersThe course will include guest lecture/-s from academia and/or industry\, will share practical insights into sustainability and circular bioeconomy\, enhancing the applicability of course concepts in real-world settings. \nDates10-14 August 2026. \nExpected frequencyTo be held once every year. \nCourse locationFrederiksberg Campus. \nCourse fee• Participant fee: 0 DKK• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: DKK 3.000 • PhD student from foreign university: DKK 3.000 • Master’s student from Danish university: 0 DKK• Master’s student from foreign university: DKK 3.000 • Non-PhD student employed at a university (e.g.\, postdocs): DKK 3.000 • Non-PhD student not employed at a university (e.g.\, from a private company): DKK 8.400 \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/quantitative-sustainability-assessment-2/ LOCATION:Department of Geoscience and Natural Resource Management CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260706 DTEND;VALUE=DATE:20260711 DTSTAMP:20260915T103004Z CREATED:20260915T103004Z LAST-MODIFIED:20260915T103004Z UID:10001981-1783296000-1783727999@ddsa.dk SUMMARY:3d Morphometrics and Image Analysis with Slicermorph DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved for PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules.Anyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nSpecial rules apply for this courseAll applicants must register for the course and also sent a motivational email to either Staffan.persson@plen.ku.dk\, or to christy.hipsley@snm.ku.dk \nAim and ContentAims:The course is a combination of formal didactics and computer labs that accompany the content. Lecturers will cover topics in theory of statistical shape analysis\, applied imaging\, and related topics. Labs will cover all aspects of conducting specimen-based\, morphology research using 3D imaging. Practical topics (e.g.\, image processing and segmentation\, visualization) will be taught using the open-source 3D Slicer visualization suite and the SlicerMorph morphometrics toolkit (statistical shape analysis). Additional lab topics include using 3D specimen repositories to obtain data\, tools and methods for collaboration and reproducible research (git and github)\, how to bridge 3D Slicer with R statistical environment for domain specific inferential analysis. Course material will focus on volumetric (e.g.\, CT or microCT) 3D datasets\, but will be equally applicable to data from 3D surface scanners.Course content over 5 days consists of morning lectures followed by afternoon hands-on practical exercises and opportunities for one-on-one training. The final day will end with student presentations. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge:• Distinguish between data types used in shape analysis• Understand landmark-based Procrustes paradigm underlying geometric morphometrics \nSkills:• Collect digital shape data from online sources• Place landmarks on 3D models• Perform Procrustes Superimposition • Visualise shape differences using PCA • Perform shape statistics in R environment \nCompetences:• Use 3D Slicer software and various modules• Produce statistical models for analysing and comparing shapes \nTarget GroupThe course is aimed at senior undergraduates\, graduate students\, post-docs and junior faculty who are interested in conducting quantitative morphological research using 3D imaging data from biological systems. It is also appropriate for more established researchers who are looking for open-source alternatives to the proprietary tools they have been using. \nRecommended Academic QualificationsEnrolled in or completed studies in Natural or Computer Sciences. Students are expected to come with a project\, ideally with sample data of their own to work on throughout the course. Each attendee should bring a recent (last two years) laptop running Windows\, Mac or Linux OSes (no netbooks or tablets). \nResearch Area3D image data such as CT\, MRI and synchrotron are increasingly incorporated into biological research\, as well as biomedical studies. While obtaining and working with such data has been historically limited to medical labs with large computational resources\, recent advances in open-source software is democratizing this process. The software 3D Slicer is now a leader in this field\, with a growing toolkit of extensions\, modules and custom software distributions tailored to the needs of diverse research communities. \nTeaching and Learning MethodsThe course is organized in morning theoretical and afternoon practical sessions. Course format will be highly collaborative\, and labs will be done in small teams. Prior experience with tools is not expected\, but will positively impact the learning experience. \nType of AssessmentShort (10 minute) student presentations on the last day\, showing some aspect of 3D image analysis covered in the course. \nLiteratureThe open-source 3D Slicer visualization suite and the SlicerMorph morphometrics toolkit (statistical shape analysis) \nhttps://www.slicer.org/ Rolfe et al. 2021. SlicerMorph: An open and extensible platform to retrieve\, visualize and analyse 3D morphology. Methods Ecol Evol. 2021;12:1816–1825. \nCourse coordinatorProfessor Staffan Persson\, Associate Professor Christy Hipsley \nGuest LecturersCourse lecturer: Associate Professor Murat Maga \nDates6th to 10th July\, 2026 \nCourse locationTBA \nRegistration deadline15 May 2026 \nCourse fee• Participant fee: DKK 0• PhD student enrolled at SCIENCE: DKK 0 • PhD student from Danish PhD school Open market: DKK 0 • PhD student from Danish PhD school not Open market: DKK 3.000• PhD student from foreign university: DKK 3.000• Master’s student from Danish university: DKK 0• Master’s student from foreign university: DKK 3.000• Non-PhD student employed at a university (e.g.\, postdocs): DKK 3.000• Non-PhD student not employed at a university (e.g.\, from a private company): DKK 8.400 \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participant type – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/3d-morphometrics-and-image-analysis-with-slicermorph/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260706 DTEND;VALUE=DATE:20260711 DTSTAMP:20260728T114833Z CREATED:20260728T114833Z LAST-MODIFIED:20260728T114833Z UID:10001870-1783296000-1783727999@ddsa.dk SUMMARY:Sustainable Livelihood Systems and Transformations in the Global South DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nRequirements for signing upRegistration requirements are a Master’s degree and relevant PhD thesis topic.Please note that all applicants will be placed on the waiting list upon registration.After registering for the course\, please send a maximum 2-page CV to the course coordinator by e-mail latest on 10. April 2026.About one week after registration deadline\, all applicants on the waiting list will be notified whether they are given a seat.Course coordinator: Ole Mertz: om@ign.ku.dk \nAim and ContentUnderstanding and enabling sustainable livelihoods is crucial as rural settings around the globe struggle with increasing climate and environmental challenges even as human development needs remain unmet. Yet rural livelihoods cannot be understood as autonomous practices; they are shaped by a complex interplay of social\, production\, market\, and governance systems and relations. These interact across scales – from international markets and global environmental systems to highly localized livelihood practices and social and environmental conditions. Studying these interactions is therefore both challenging and extremely important for understanding sustainable livelihoods and futures in rural areas across the globe. This course therefore equips students with an understanding of rural livelihood transformations from a systems perspective\, with focus on Global South contexts. It provides insights into how livelihood transformations and outcomes are shaped by social\, environmental\, economic and governance relations and systems. It examines:– The impact of climate and environmental conditions on livelihood options\, risk and producer perceptions and strategies– The role of social relations and transformations in shaping livelihood change\, including socio-cultural changes\, rural-urban relations and mobilities– The role of production and market relations and systems\, including value and commodity chains and access to markets– The role of governance systems and policies\, including formal state institutions\, programs and practices; de facto governance through\, e.g.\, market and private sector institutions; and producer organization and institutions– Differentiated social\, environmental and economic outcomes of livelihood transitions and transformations \nThe course provides participants with latest knowledge on these aspects shaping livelihood transformations in the Global South and understanding of how they interact in practice. It also provides analytical and conceptual tools that can be used to analyze livelihood transitions and transformation from a systems perspective\, as well as applied experience using these tools. The course can therefore support PhD students from the early to later stages of the PhD process\, from developing their analytical and theoretical approach to strengthening their analysis. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge:• Overall system factors and relations shaping rural livelihood transitions and transformation in Global South settings\, including social\, environmental\, economic and governance aspects• Empirical examples and applied understanding of systems factors and cross-scalar interactions in practice• Livelihood transition and transformation outcomes\, including sustainability outcomes from social\, environmental and economic perspectives and differentiated outcomes across socio-economic groups• Theoretical\, analytical and conceptual approaches for understanding livelihood transition and transformation from a systems perspective \nSkills:• Identifying systems factors and interactions shaping livelihood transitions and transformations• Understanding and analyzing the cross-scalar nature of livelihood systems and transitions• Integrating sustainability and livelihood analyses into broader social\, environmental\, economic and governance systems perspectives \nCompetences:• Ability to analyze complex\, cross-scalar systems shaping livelihood transitions and transformations• Critical understanding of the factors shaping sustainable livelihoods and differentiated outcomes• Ability to apply systems thinking\, including relevant theoretical\, analytical and conceptual approaches\, to studies of rural livelihood and development \nTarget GroupThe course is relevant to all PhD students interested in sustainability\, livelihoods and development in Global South countries. It is suitable for a broad range of academic fields and methodological approaches\, including Geography\, Environmental Science\, Environment and Development\, Area Studies\, and other disciplines where interdisciplinary aspects of livelihoods and sustainability are relevant. It is well suited for students across stages of their PhD – from project development to analysis and writing-up. \nRecommended Academic QualificationsStudents must possess a Master’s degree to participate in the course and should be enrolled in a PhD programme. If there are interested applicants that do not meet these criteria\, they may apply. However\, applicants meeting the course requirements will be prioritized. \nResearch AreaRelevant research areas include sustainable livelihoods and development; sustainable production systems; agriculture and rural livelihoods; climate change adaptation and resilience; resource management; biodiversity and conservation; market conditions and relations; value and commodity chains; and politics\, governance and institutions.Geography\, Environmental Science\, Environment and Development\, Area Studies\, and other disciplines addressing the interdisciplinary nature of livelihoods\, environment and sustainability. \nTeaching and Learning MethodsTeaching and learning methods include lectures\, applied groupwork and exercises\, and active in-course feedback. \nType of AssessmentAfter the course\, each participant will write up and submit an individual reflection paper. This paper will document and reflect on what each student learns during the course. \nLiteratureLiterature will be defined in collaboration with the international instructors and will be shared with the participants ahead of the course. \nCourse coordinatorOle Mertz\, professor\, Section for Geography – Land\, Environment and Society\, Department of Geosciences and Natural Resource Management. \nGuest Lecturers– Assoc. Prof. Le Thi Hoa Sen\, Hue University of Agriculture and Forestry\, will provide input on rural transformation with focus on changing livelihood and social relations in areas affected by acute climate risk and resource degradation. – Prof. Jesse Ribot\, American University\, will provide input on the role of market structures\, relations and governance in shaping rural livelihoods and vulnerability in settings experiencing poverty and climate change.– Prof. Jonathan Rigg\, University of Bristol\, will provide input on processes of livelihood transitions including coping and resilience\, hazards and disasters\, rural-urban relations\, and migration and mobility.– Senior Researcher Lily Lindegaard\, Danish Institute for International Studies\, will provide input on sustainable rural transformation from a governance and institutional perspective. \nDatesJuly 6-10\, 2026 \nCourse locationThe course will be held at the Department of Geosciences and Natural Resource Management\, Øster Voldgade 10 1350 København K \nCourse fee• Participant fee: 0 DKK• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 3000 DKK• PhD student from foreign university: 3000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 3000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/sustainable-livelihood-systems-and-transformations-in-the-global-south/ LOCATION:Department of Geoscience and Natural Resource Management CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260629 DTEND;VALUE=DATE:20260704 DTSTAMP:20260728T114833Z CREATED:20260728T114833Z LAST-MODIFIED:20260728T114833Z UID:10001782-1782691200-1783123199@ddsa.dk SUMMARY:Introduction to Nutritional Metabolomics DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved for PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and ContentThe aim of this course is to introduce the student to all phases in a nutritional metabolomics study\, to instruct the students on sample handling\, and to train the student in data analysis and in the use of freely available tools for the metabolomics data flow. \nThe course will provide a general overview of LC-MS based untargeted metabolomics from study design to results and will be exemplified with its specific application in nutrition. It will be delivered using a mixture of lectures\, hands-on data preparation and analysis\, computer-based practical sessions\, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but there is no practical lab work. \nThe students will go through common steps in a typical metabolomics study using a real-life case. This case study includes plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF have been conducted and the students will further process and analyze the acquired data with various freeware tools (e.g. R\, XCMS\, MZmine etc). They will finally work on identification of relevant metabolites using manual analysis assisted by several web-based databases and structure elucidation tools. The course will be concluded by presentations of reports generated by the students based on the case study. \nThe students should expect a fairly technical course with a strong focus on the hands-on data analysis abilities and data interpretation skills. Programming skills are not a prerequisite for entering the course and students are guided through the exercises. However\, for students that are not familiar with R we expect them to explore the self-study curriculum based on short videos and texts that cover essential programming concepts.The project work has a high workload\, and hence evening work can be expected during the course week. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge:• Analyze different types of study designs commonly used in metabolomics (e.g.\, interventional study\, case-control\, cohort\, cross-sectional) and evaluate their strengths and weaknesses in terms of validity\, bias\, and applicability.• Explain each step of the metabolomics pipeline\, including sample collection\, data acquisition\, preprocessing\, statistical analysis\, and interpretation\, and identify appropriate tools and methods to perform each step effectively. \nSkills:• Carry out data preprocessing using freely available tools (R/XCMS)• Perform basic univariate and multivariate analysis (e.g. R)• Interpret the MS/MS spectra by manual interpretation and by utilizing available tools (e.g. MetFrag\, SIRIUS) and databases (e.g. HMDB\, METLIN and MassBank) \nCompetences:• Describe the handling of urine\, plasma and other samples collected from humans for metabolomics analysis• Understand the basic principles of UPLC-QTOF technology• Suggest which sample type to analyze for a specific research question and propose the relevant sample collection and preparation procedure \nTarget GroupAll PhD students that have metabolomics as part of their projects. That is from medical applications to nutrition\, food or microbiome. \nRecommended Academic QualificationsBasic knowledge of chemistry\, statistics and biochemistry. \nResearch AreaMetabolomics\, chemistry\, biology\, statistics \nTeaching and Learning MethodsBasic principles and an overview are given by mostly frontal lectures that include small tasks and quizzes.Each practical step is then explained\, and the students work in groups to perform the tasks on the dataset provided.The course culminates in a presentation where the students describe the steps they took and the conclusions they reached. \nType of AssessmentActive participation in the group work and final presentation. \nCourse coordinatorJan Stanstrup \nGuest LecturersJan StanstrupHenrik Munch Roager Giorgia La BarberaCarl Brunius \nDates2026-06-29 – 2026-07-03 \nExpected frequencyYearly in the last week of June / first week of July. \nCourse locationNEXS\, Frederiksberg \nRegistration No formal requirements. Adequate background is checked and clarified if needed. Adequate is considered having basic training in statistics\, chemistry and biology or biochemistry and a well-defined interest in metabolomics. \nCourse fee• Participant fee: 1800 DKK• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 3000 DKK• PhD student from foreign university: 3000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 3000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/introduction-to-nutritional-metabolomics-2/ LOCATION:Department of Nutrition Exercise and Sports CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260623 DTEND;VALUE=DATE:20260628 DTSTAMP:20260915T103005Z CREATED:20260915T103005Z LAST-MODIFIED:20260915T103005Z UID:10001977-1782172800-1782604799@ddsa.dk SUMMARY:Plant beneficial and plant pathogenic microorganisms DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nRequirements for signing upApplicants must apply for the course and also send submit half page motivation letter and support letter from their supervisor. The letter must be sent to Flemming Ekelund by the latest on 10 May 2026 (fekelund@bio.ku.dk) \nAim and ContentThe course aims to introduce the students to plant-associated microorganisms through theoretical and practical engagement with experts in the field of microbiome research in plants and other hosts in terrestrial ecosystems. We aim to unravel the role of microorganisms in the One Health concept\, i.e. to teach students about the role of microorganisms in sustainable and unified optimization of health of people\, animals and plants. We will tackle the topics such as the role of microorganisms in plant and animal systems\, comprising both symbiotic and antagonistic relationships. We will focus on belowground plant-microbe and microbe-microbe interactions. Major topics will be plant-pathogen interactions and beneficial plant-microbiome interactions. We will also comprise the drivers of plant- and animal-microbiome interactions such as global change factors and intrinsic host-microbiome associations. We will round up the course with knowledge on the management practices for a sustainable plant growth. \nOur course aims to bridge the gap between the theoretical studies and research experience. Therefore\, we aim to organize practical exercises for the students along with the lectures. We plan to start our course with a poster session where the students will introduce themselves and their own work. We aim to distribute the relevant literature to the students and ask them to do their own literature research for the course. The course will end with a presentation of the practical work and an engaging discussion on how the acquired knowledge from the course can be applied to the students’ own work. \nThe course is relevant if you work with plants and soil in the context of environmental\, agricultural or natural science. E.g.: (1) studies on the plant and animal holobiont\, (2) plant-microbe interactions (pathogenic and beneficial)\, (3) microbe-microbe interactions in the context of plant performance and nutrient cycling\, (4) work on new methods to identify specific groups of organisms in situ\, or (5) soil food web analysis in the context of plant performance. The lectures and discussions will stimulate the interaction between teachers and participants and are essential for a successful course \nWe cover the following topics: The holobiont: Concept of the holobiont; host-microbe interactions; plant holobiont. Plant-microbe interactions: pathogenic and beneficial plant-microbe interactions. Soil microorganisms and microbe-microbe interactions: The structure and function of microbial communities; diversity of soil bacteria; mycorrhiza; protozoa\, nematodes and their relevance in plant performance. Molecular biology and bioinformatics: a linkage between plant-microbe interactions\, microbial ecology\, and organism-biology using bioinformatics. \nA major theme in the course will be the bridge between the good hypothesis\, the well-planned experiment and the firm discussion and conclusion. Student group work will focus on this and it will be the topic of group presentation and the final written assignment. Many scientific papers are unfocussed and difficult to follow and we hope to improve that. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge:• Holobiont – relevance\, methods and application• Plant-microbe interactions in the rhizosphere and aboveground• Nematode-protist-microbe interactions• Sustainable management practices for plant growth \nSkills:• Literature review• Scientific writing and presentation• Structure of scientific review• Ability to design an expriment/project• Competences:• Individual and team work• Project management• Bridging and application of acquired knowledge to students’ own work \nTarget GroupPhD students working with microbiome related research \nRecommended Academic QualificationsMaster of Science in biology\, microbiology\, nematology or similar qualifications \nResearch AreaBiology\, plant pathology\, microbiology\, nematology \nTeaching and Learning MethodsLectures\, exercises\, individual and group work\, excursions\, data analyses\, report writing \nType of AssessmentAttendancePoster presentationPresentation of group workReport on and critical evaluation of the teaching \nLiteratureState of the art scientific papers \nCourse coordinatorFlemming Ekelund \nLecturers– Flemming Ekelund (Department of Biology\, University of Copenhagen)– Rasmus Kjøller (Department of Biology\, University of Copenhagen)– Anders Priemé (Department of Biology\, University of Copenhagen)– Nikolaj Kindtler (Department of Biology\, University of Copenhagen)– Rute Andreia Rodrigues da Fonseca (GLOBE Institute\, University of Copenhagen)– Frederik Bak (Department of Plant and Environmental Sciences\, University of Copenhagen)– Alexandr Gavrin (Department of Molecular Biology and Genetics\, Aarhus University)– Enoch Narh Kudjordjie (Department of Agroecology – Plant Pathology and Microbiology\, Aarhus University)– Mette Vestergård (Department of Agroecology – Plant Pathology and Microbiology\, Aarhus University)– Ahmed Abdelfattah (Leibniz Institute of Agricultural Engineering and Bio-economy)– Olivera Topalovic (INRAE\, IGEPP – Institute for Genetics\, Environment and Plant Protection) \nDates23-27 June 2026:23 – 27 June: Lectures\, discussions\, student poster presentations. 27 June: Student project presentations\, evaluation\, and departure \nCourse locationCampus North\, Department of Biology\, University of Copenhagen \nRegistration deadline10 May 2026Applicants must apply for the course and also send submit half page motivation letter and support letter from their supervisor. The letter must be sent to Flemming Ekelund by the latest on 10 May 2026 (fekelund@bio.ku.dk) \nCourse fee• Participant fee: 0 DKK• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 3000 DKK• PhD student from foreign university: 3000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 3000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/plant-beneficial-and-plant-pathogenic-microorganisms/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260602 DTEND;VALUE=DATE:20260611 DTSTAMP:20260624T091135Z CREATED:20260624T091135Z LAST-MODIFIED:20260624T091135Z UID:10001840-1780358400-1781135999@ddsa.dk SUMMARY:Introduction to programming in R for biologists DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nExternal course website\nFor more information about the course\, please see the course website: https://programmingintrocph.weebly.com/ \nAim and Content\nThe purpose of the course is to provide an introduction to programming in R for PhD students in biology (particularly the fields of ecology and evolution). \nProgramming (such as coding in R) is by now an essential tool in biological research\, including in PhD projects. However\, biology degrees often do not include an actual introduction to programming\, and available courses from other degrees are often targeted at students with a high-level mathematical background\, making them unsuitable for biology students. \nThis course is developed specifically for PhD students in biology. The philosophy behind the course is that instead of just learning to run pieces of code (e.g. statistical tests)\, the participants will get an understanding of the fundamentals of programming\, which will give them a strong base for using programming flexibly and correctly for their own scientific purposes. \nThe course will consist of a mix of lectures that will introduce main programming concepts and procedures\, and exercises that will give the students hands-on experience in programming. Throughout the course\, the concepts and exercises will be related to biologically relevant examples. \nThe course will furthermore include talks by an international researcher\, who will describe their own research and show examples of how they used programming in their projects and relate this to the programming concepts introduced in the course. This will provide the students with real-life examples of how programming is used in biological research. \nThe students will also get the opportunity to work on code for their own projects with support from the course teachers. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\n• Key programming concepts\, including variables\, types\, data structures\, functions\, loops\, control statements\, and debugging\n• Programming procedures\, including script development\, script structuring\, and good coding practices.\n• The R programming language and the Rstudio environment\n• How programming is used in biological research \nSkills:\n• Creating variables and manipulating them via subsetting and substitution\n• Constructing and using functions\n• Constructing and using loops and control statements\n• Error correction and debugging\n• Efficient script development\, including using pseudocode\, commenting\, and structuring by functions. \nCompetences:\n• Creating and using well-structured scripts\, from planning to coding and execution\n• Using programming to store and manipulate biological data \nTarget Group\nThe course is aimed at PhD students from the fields of ecology and evolution\, and particularly behavioural ecology.\nPhD students from other areas can also benefit from the course.\nThe programming skills that are learned in the course are general and thus relevant to any field where programming is used. \nRecommended Academic Qualifications\nThe participants are expected to be enrolled in a PhD program. While PhD students will be prioritized\, master students and postdocs will also be considered.\nThe course is introductory and no prior programming skills or familiarity with R is required.\nStudents that have used R before can still benefit from the course if they have not formally learned programming. \nResearch Area\nThe general programming skills that are the focus of this course are relevant for any research area where programming is used. The course is mainly aimed at the fields of ecology and evolution (and particularly behavioural ecology)\, but is also relevant for other research areas. \nTeaching and Learning Methods\nLectures: The lectures will introduce key concepts of programming. This will provide the participants with knowledge about main aspects of programming\, which they will get experience with during the subsequent exercises.\nExercises: The exercises will relate directly to the programming concepts introduced in the lectures and will give the students hands-on experience in programming. The exercises will be of different types to suit different learning styles.\nResearch lectures: An international guest lecturer will describe their own research and show examples of how they used programming in their projects and relate this to the programming concepts introduced in the course. This will provide the students with real-life examples of how programming is used in research.\nProject support: The students will get the opportunity to work on code for their own projects with support from the course teachers (this will constitute a limited part of the course). \nType of Assessment\nActive participation in the course and physical presence over at least 6 of the 7 days. \nCourse coordinator\nCourse coordinator: Associate Professor Elodie Mandel-Briefer \nCollaborating partner and lecturer: Assistant Professor Josefine Bohr Brask\, affiliated with the section of the course coordinator (Section for Ecology and Evolution\, BIO\, SCIENCE\, KU)\, and assistant professor at SODAS (Copenhagen Center for Social Data Science\, SAMF\, KU). \nGuest Lecturers\nErin Siracusa is a postdoctoral researcher at the University of Exeter\, in the Centre for Research in Animal Behaviour\, one of the most important hubs for behavioural ecology worldwide. Erin does innovative research at the forefront of animal social behaviour. Using long-term datasets from squirrels\, monkeys and sheep\, she investigates how the aging of individuals in populations of wild animals affect their social behaviour and social environment. In her research she uses programming for a range of purposes\, including data management\, statistical analysis\, and agent-based modelling. She will contribute to the course by presenting her research and providing examples of how she has used programming in her projects. She will also assist in other parts of the course including guiding students through exercises and providing support for independent projects. \nDates\n2.6-10.6.2026 (7 days in total\, excluding the weekend) \nExpected frequency\nOnce every two years. \nCourse location\nNørre campus \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 4200 DKK\n• PhD student from foreign university: 4200 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 4200 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 4200 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 11.760 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/introduction-to-programming-in-r-for-biologists-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260525 DTEND;VALUE=DATE:20260613 DTSTAMP:20260902T103741Z CREATED:20260427T114836Z LAST-MODIFIED:20260902T103741Z UID:10001807-1779667200-1781308799@ddsa.dk SUMMARY:Biological IR and Raman Imaging DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved for PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and ContentThis advanced imaging course module gives a hands-on introduction to chemical imaging of biological and environmental samples using IR and Raman micro-spectroscopy. In this way\, the course aims to make students capable of integrating IR and/or RAMAN imaging in their projects. Depending on the needs of the enrolled students\, the focus of the course can be broader than only exploiting environmental and/or biological samples. \nThe course gives an introduction to vibrational micro-spectroscopy\, an overview of what can and cannot be detected by these techniques\, and how they complement each other. In the course\, we go through all steps of selecting analysis methods\, sampling\, and preparing specimens\, carrying out measurements\, processing data\, and presenting results. Especially\, we will discuss representability issues for heterogeneous samples\, the spatial and spectral resolutions obtainable\, and the compromises necessary when utilising micro-spectroscopy within research. Instrument demonstrations and hands-on training will be included in the course. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge:• Able to give an overview of the molecular basis of vibrational spectroscopy• Describe properties and characteristics of infrared radiation• Describe how molecular vibrations can be detected and quantified by interaction with light \nSkills:• Read\, understand and evaluate scientific papers that utilize IR and/or RAMAN imaging within the student’s own field• Design a sampling and sample preparation methodology for vibrational microscopy based on known material properties and known characteristics and limitations of the included methods• Make a flow-chart of data management from raw IR/Raman image data to analysis and presentation of results \nCompetences:• Able to describe the difference between IR and RAMAN imaging• Judge suitability of these imaging modalities to solve scientific questions within the student’s own field of research• Design a procedure based on vibrational microscopy aimed at addressing a research question relevant to the student’s own field of research \nTarget GroupAll students planning to use IR and/or Raman imaging in their projects. \nRecommended Academic QualificationsOpen for master and PhD students after taking the UCPH’s basic Biological Imaging module (Biological Imaging – Basic Module” (Professor Alexander Schulz\, PLEN)) or if the relevant knowledge was acquired in another way\, e.g.\, through related courses at UCPH or other universities. If in doubt\, please contact lgt@ign.ku.dk. \nResearch AreaRelevant for students within Environmental science\, Material science\, Biology\, Biology-Biotechnology\, Medicine and Technology\, Molecular Biomedicine\, Nanoscience or Pharmaceutical Sciences. \nTeaching and Learning MethodsLectures\, demonstrations\, hands-on practical exercises\, theoretical exercises.Students will prepare samples\, perform measurements\, analyse data and present their work in a written report. \nType of AssessmentWritten report. The report must be evaluated as “passed” for the course to be completed. \nCourse coordinatorLisbeth G. Thygesen \nGuest LecturersTBD \nDates3 last weeks of block 4. \nExpected frequencyTo be held every 2nd year \nCourse locationFrederiksberg Campus \nCourse fee• Participant fee: 1000 DKK• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 3000 DKK• PhD student from foreign university: 3000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 3000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/__trashed-142/ LOCATION:Department of Geoscience and Natural Resource Management CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260525 DTEND;VALUE=DATE:20260611 DTSTAMP:20260902T103741Z CREATED:20260427T114808Z LAST-MODIFIED:20260902T103741Z UID:10001724-1779667200-1781135999@ddsa.dk SUMMARY:Biological Mass Spectrometry Imaging DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and ContentThis advanced biological imaging course module gives a theoretical and hands-on introduction to metabolite imaging of biological samples using mass spectrometry imaging. The course aims to introduce the basic concepts of the technology\, including choice of appropriate approach for a given sample type.The course gives an introduction to mass spectrometry imaging and the different types of instruments within this technology and provides an overview of strengths and weaknesses of the methods\, including what can and cannot be detected\, and how they complement each other. In the course\, we go through all steps of selecting appropriate methods\, sampling and preparing specimens\, and sample analysis and data analysis. These steps will all be demonstrated in the hands-on part of the course. While this part will mainly focus on plant and possibly microbial samples\, we will also explain which approaches to sample preparation are appropriate for different types of specimens and demonstrate some that work well for animal samples. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge• Understand basic concepts of mass spectrometry imaging (MSI)• Know different approaches to sample preparation and MSI analysis and how/why they are appropriate for specific sample types• Understand the possibilities and limitations of the technology \nSkills• Read\, understand and evaluate scientific papers that utilize MSI within the student’s own field• Design a MSI experiment with sampling\, sample preparation and sample analysis based on knowledge of the sample type\, metabolite(s) of interest and characteristics and limitations of the chosen methods• Carry out basic sample preparation • Carry out basic MSI data analysis and understand how the results can and cannot be interpreted \nCompetences• Able to explain the different approaches and/or instruments for sample preparation and MSI analyses• Able to evaluate the suitability of the different approaches to answer biological questions within the student’s own field of research• Able to advice on general suitability of the different approaches to answer scientific questions in other biological sciences \nTarget GroupStudents that either: – Have MSI instruments at their home institution and plan to use it in their projects\, or…– Are interested in initiating collaboration with the host lab\, the PlantMSI facility headed by the course responsible (mainly plant science students)\, or…– Use other mass spectrometry technologies in their science and want to learn about MS imaging \nRecommended Academic QualificationsOpen for master and PhD students after taking the UCPH’s basic Biological Imaging module (course number X) or if the relevant knowledge was acquired in another way\, e.g.\, through related courses at UCPH or other universities. If in doubt\, please contact nnb@plen.ku.dk. \nResearch AreaRelevant for students within Plant Science\, Biology\, Biology-Biotechnology\, Medicine and Technology\, Molecular Biomedicine\, Pharmaceutical Sciences. \nTeaching and Learning MethodsLectures\, demonstrations\, hands-on practical exercises\, theoretical exercises.Students will prepare samples\, analyze samples and data and present their work in a written report. \nType of AssessmentWritten report. The report must be evaluated as “passed” for the course to be completed. \nLiteratureRelevant background material and journal articles will be electronically available at the start of the course. \nCourse coordinatorNanna Bjarnholt\, Associate Professor\, nnb@plen.ku.dk \nGuest LecturersAssociate Professor Christian Janfelt\, Dept. of Pharmacy\, SUND\, KU. Lectures on animal MSI and specific types of instrumentation.Possible also guest lecturers from SDU (protein MSI) and/or DTU (microbial MSI) \nDates25 May – 10 June 2026 (last 3 weeks of block 4). \nExpected frequencyTo be held every year \nCourse locationFrederiksberg Campus \nCourse fee• Participant fee: 750 DKK – All participants are charged for instrument-time & laboratory expenses for this course module.• PhD student enrolled at SCIENCE: 0 DKK• PhD student from Danish PhD school Open market: 0 DKK• PhD student from Danish PhD school not Open market: 3000 DKK• PhD student from foreign university: 3000 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 3000 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy• Cancellations made up to two weeks before the course starts are free of charge.• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000• No-show will result in a fee of DKK 5.000• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant feePhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.In addition to the course fee\, there might also be a participant fee.If the course has a participant fee\, this will apply to all participants regardless of participanttype – and in addition to the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/__trashed-141/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260518 DTEND;VALUE=DATE:20260523 DTSTAMP:20260902T103742Z CREATED:20240821T085103Z LAST-MODIFIED:20260902T103742Z UID:10001348-1779062400-1779494399@ddsa.dk SUMMARY:Fundamentals of the PhD education at SCIENCE - module 2 - K10 DESCRIPTION:Enrolment guidelines \nThe module is mandatory for all new PhD students enrolled at Science from 1 January 2024 as part of the course ‘Fundamentals of the PhD education at SCIENCE’. \nIf you are a double degree student with a foreign host\, you can not enroll in this course. \nLearning objectivesThe learning outcomes for the segments are given below. Each outcome is marked K for Knowledge\, S for Skills\, or C for Competences. \nData Management segment:• Identify relevant legislation\, requirements\, and policies on data management applicable to research projects at UCPH (K).• Recognize recommendations and requirements regarding open and reproducible research designs\, data collection\, and data publication (K).• Classify data and conduct a risk assessment to ensure the secure storage of data (S).• Assess how data can be preserved and shared to guarantee FAIR use of data (S).• Assess when to use electronic lab notes (S).• Contribute to planning and conducting appropriate data and materials management in all phases of their PhD project (C). • Be able to adhere to best practices of open and reproducible research (C).Career Management segment:• Differentiate typical career paths for SCIENCE PhDs (K).• Describe selected understandings of motivation (K). • Explore and explain their career priorities (S).• Build a professional profile on LinkedIn (S).• Assess how different uses of their change of scientific environment may impact their career (S).• Integrate knowledge of typical career paths for SCIENCE PhDs with an understanding of their personal career priorities (C).• Develop a personal networking strategy and build a professional network that supports their career interests (C).Data Science segment:• Know Data Science as a research methodology (K). • Apply basic Statistics or Machine Learning computational frameworks and methods when appropriate in their research (S).• Build a network for potential inter-disciplinary data science collaborations (C). \nContentThe purpose of the course is to introduce the students into Data Science\, Data Management\, and Career Management:• Many students will apply/develop Data Science methods (data analysis\, statistics\, and machine learning) directly in their own research; and all students should be aware of this potential. • Some students will directly apply Data Management principles\, and all students should be aware of the general policies.• All students should actively manage their careers. \nThe module consists of 10 morning/afternoon sessions during the on-campus course week (2 on Data Management\, 1 on Career Management\, and 7 on either Machine Learning or Statistics). Prior to course start\, the students will choose to follow either the Statistics or Machine Learning variant of the module. \nThe aim of the Data Management segment is to equip PhD students with knowledge and skills to:• Manage data and primary materials responsibly during their PhD projects.• Create open and reproducible research outputs. \nThe aim of the Career Management segment is that PhD students start to explore their values\, motivation\, and the great variety of career options that are open to them after their PhD. The segment will cover:• Megatrends in the labour market and typical career paths for PhDs from the natural sciences.• Motivation\, values\, and career priorities.• Change of scientific environment.• Networking for career development. \nThe Data Science segment will aim ensure that PhD students will consider Data Science as a methodology and allow them to apply basic Statistics or Machine Learning methods when appropriate in their research. \nThe Statistics variant will cover:• Statistical thinking and methodology\, including statistical power and principles for experimental design.• Statistical modelling using fixed and random effects.• Tabular and graphical presentation of experimental results and statistical analyses.• The R programming environment for Statistics and basic comparisons to other statistical software. \nThe Machine Learning variant will cover:• Machine learning foundations and methods.• Data Science caveats and best practice.• Introduction into AI with focus on intuitive understanding the big models in terms of potential\, applicability\, limitations\, and sustainability.• Application of AI and machine learning in science. \nParticipantsAll PhD students at SCIENCE as part of the PhD Fundamentals course. Module 2 is about 6 months into the PhD programme. \nLanguageEnglish \nFormThe module will include a mixture of lectures\, exercises\, plenary and group discussions\, and peer feedback during the on-campus segments. Around the on-campus week\, the students will perform eLearning as well as reflective and practical assignments. \n Type of assessmentAll segments require presence and active participation.The students must hand in a report where they choose between a Data Management Plan\, or a report related to their Statistics or Machine Learning specialization. \nRegistrationAutomatic registration when enrolling in the PhD program at SCIENCE. \nRemarksThe PhD School at the Faculty of SCIENCE is committed to building a learning environment that welcomes\, includes\, and empowers all its PhD students. By building a Faculty-wide peer community of PhD-students with the Fundamentals course\, we secure that all PhD candidates are given adequate instruction in a range of essential competences that lie outside the core scientific research skills offered through supervision\, tool-box and specialized PhD courses. Moreover\, we build bridges between different research programmes at the Faculty of SCIENCE and offer diverse\, multidisciplinary fora of exchange strengthening the PhD candidates’ scientific and social networks and laying the foundation for a strong alumni culture. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/fundamentals-of-the-phd-education-at-science-module-2-k10/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260506 DTEND;VALUE=DATE:20260507 DTSTAMP:20260902T103743Z CREATED:20260420T110319Z LAST-MODIFIED:20260902T103743Z UID:10001722-1778025600-1778111999@ddsa.dk SUMMARY:Responsible Conduct of Research 2: Getting Ready for Submission of Manuscripts and Thesis DESCRIPTION:Content \n \n”Special rules apply for this course”\n \nThis course is ONLY for PhD students enrolled at SCIENCE in the second or last year of their PhD Plan. All other applicants will get a rejection. \nWhen submitting a paper or a PhD thesis one is typically confronted with a great deal of responsible conduct of research issues. These are issues on potential plagiarism\, open access requirements\, data management\, authorship issues\, documentation of ethical and legal permissions\, declaring one’s conflicts of interest\, etc. It is not always easy to navigate in these issues\, covering a web of different institutions’ expectations\, legal requirements\, and societal and academic norms. Many PhD students are therefore taken by surprise by these matters on the night before deadline. In this course we therefore guide the PhD student through the most general aspects of these issues as they appear in the process of submitting a paper or a PhD thesis. Furthermore\, participation in the course gives access to a pre-screening of the PhD thesis for potential plagiarism prior to submission. \nCourse purposeHelping the PhD student to submit\, responsibly and successfully\, the PhD student’s thesis as well as papers to scientific journals. \nCourse targetThe course is open to all PhD students at the Faculty of SCIENCE\, University of Copenhagen\, and is mandatory for all PhD students enrolled after 1 August 2020 University of Copenhagen. The course is intended for PhD students who expect to submit their PhD thesis within 12 months. \nLearning objectives– Understand what it takes to stay clear of plagiarism and selfplagiarism. – Understand and interpret the result of iThenticate’s screening for duplicate text.– Understand and apply relevant legal\, scientific and societal norms (of e.g. but not only Responsi-ble Conduct of Research) in regards to the following specific subjects:* Plagiarism and selfplagiarism * Open access * Authorship* Conflicts of interests* Ethical and legal permissions* Data management \nRequirements and expectationsPrior to the course the PhD student must have some draft of a paper intended to be submitted to a scientific journal\, or (if the PhD student is writing a monography) some draft of a dissertation. The course is intended for PhD students who expect to submit their PhD thesis within 12 months. We recommend that the PhD student has passed the mandatory RCR1 course prior to this course. \nFormat of the course4 hours course with teacher presentations and exercises directly aligned with the mandatory as-signment. \nContent of the course– (Self-)plagiarism issues – Interpretation of iThenticate results– Open Access issues– Authorship issues– Conflicts of interest issues– Issues relating to ethical permissions– Data Management issues– Other issues \nAssignmentThe PhD student is given a list of challenges to guide the PhD student’s submission(s) of papers and thesis. The PhD student’s assignment is to fill out a logbook following the list and\, for each checkpoint\, explain how the PhD student is dealing with the challenge\, or how the PhD student intends to deal with the challenge. One week after the course the PhD student submits the log-book to the teacher of the course. Two weeks after submission of the logbook\, the PhD student receives a notification of the logbook being approved or not approved. If not approved the PhD student will receive an explanation and be asked to revise and re-submit within two weeks. All exchange of logbooks happens via Absalon and all parts of this assignment are mandatory.The PhD student has three attempts to pass the assignment. If the assignment is not passed after three attempts\, the PhD student must take the course again. Failure to meet a deadline results in a failed attempt. \nECTSParticipation in this course will be granted 1 ECTS. \nLimitationEach course has no more than 25 participating PhD students. The course is offered 5-6 times each year. \nCourse Director and locationProfessor Peter Sandøe\, Department of Food Resources and Economics andDepartment of Veterinary and Animal Science. \nThe course takes place at Frederiksberg Campus or ONLINE from 12:00 to 16:00 \nEnrolment guidelines \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/__trashed-134/ END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260504 DTEND;VALUE=DATE:20260509 DTSTAMP:20260309T141452Z CREATED:20260205T121650Z LAST-MODIFIED:20260309T141452Z UID:10001717-1777852800-1778284799@ddsa.dk SUMMARY:Estimating Causal Effects with Observational Data DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nResearchers are often interested in investigating causal relationships\, i.e.\, if and how one variable affects another. While the analysis of causal relationships is ideally done using experimental data\, in several research areas (e.g.\, social sciences) experiments are often infeasible or suffer from important limitations. As a result\, most empirical studies in the social sciences and related research areas are based on observational (i.e.\, nonexperimental) data.\nParticipants in this course will learn state-of-the-art methods used for investigating causal relationships with observational data. Course participants will also learn how to evaluate and discuss the appropriateness of research designs (“identification strategies”) and empirical methods for analysing causal relationships\, and they will learn to choose the most appropriate research designs and empirical methods for a specific research question. This will help participants obtain more credible and reliable results in their own research.\nTopics taught in this course include causal directed acyclic graphs (DAGs)\, methods based on ‘selection-on-observables’\, methods based on instrumental variables\, synthetic control methods\, regression discontinuity designs\, difference-in-differences\, methods for panel data with staggered treatment\, and causal machine learning methods. The course participants will learn the theoretical background and underlying assumptions of these methods as well as how to apply them in real-world analyses. \nLearning outcomes\nIntended learning outcomes for the students who complete the course: \nKnowledge\n• Understand causal DAGs.\n• Describe methods for causal inference with observational data\, including\n• ‘Selection-on-observables’\n• Instrumental variables\n• Synthetic control\n• Regression discontinuity designs\n• Difference-in-differences\n• Methods for panel data with staggered treatment\n• Causal machine learning\n• Describe the assumptions that need to be fulfilled if the methods listed above should give reliable estimates of causal effects. \nSkills\n• Construct and interpret causal DAGs and use them to identify causal effects using the DAGitty software.\n• Apply methods for causal inference with observational data using (statistical) software such as R\, Stata\, or Python.\n• Assess to which extent assumptions that are required by different causal inference methods with observational data are fulfilled in specific real-world applications. \nCompetences\n• Choose research designs and methods that are appropriate for causal inference with observational data in their research area.\n• Critically evaluate the appropriateness of research designs (“identification strategies”) and methods for answering causal questions with observational data in their research area (this refers to their own research\, e.g.\, when discussing strength and weaknesses of causal analyses in their own papers\, as well as to the research done by others\, e.g.\, when reviewing manuscripts or assessing the reliability of causal analyses). \nTarget Group\nPh.D. students at SCIENCE\, SUND\, SAMF\, and other faculties or universities\, who aim to investigate causal questions with observational data (e.g.\, economists\, other social scientists\, nutritionists\, epidemiologists\, other health/veterinary scientists\, etc.). \nRecommended Academic Qualifications\nThe students should have basic knowledge in statistics (e.g.\, hypothesis tests\, ordinary least-squares (OLS)\, etc.) obtained\, e.g.\, in the statistics variant of the PhD course “Fundamentals of the PhD education at SCIENCE – module 2” or a similar course. \nResearch Area\nAll research areas that apply statistical methods to answer causal research questions with observational data\, including economics\, other social sciences\, nutritional sciences\, epidemiology\, other health/veterinary sciences\, etc. \nTeaching and Learning Methods\nThe course participants are encouraged to read some of the course material before the course starts to be well prepared. The course consists of a combination of lectures and practical exercises. The participants will construct and interpret causal DAGs and they will learn to implement various methods for estimating causal effects. While the teachers will use the R software to present solutions to these exercises\, the participants are free to use other software (e.g.\, Stata or Python). The practical exercises also include group discussions\, e.g.\, about the appropriateness of research designs (“identification strategies”) and empirical methods. The course participants can choose to write a short report (5-10 pages)\, in which they apply at least one of the methods taught in the course to simulated or real-world observational data\, e.g.\, as a part of their PhD project. Reproducibility of the empirical analysis will play a key role in the lectures\, the practical exercises\, and in the ‘short report’ (exam). \nType of Assessment\nThe participants get this course approved with 2.5 ECTS if they attend the lectures\, do the practical exercises\, and pass a multiple-choice test given at the end of course.\nThe participants get this course approved with 5 ECTS if they additionally write and submit a short report (see above) that is positively assessed by the teachers. This short reported has to be submitted to the course coordinator no later than 3 months after the end of the course. \nLiterature\n• Angrist\, J.D. and Pischke\, J.-S. (2009)\, Mostly Harmless Econometrics\, Princeton University Press.\n• Angrist\, J. D. and Pischke\, J. S. (2014). Mastering ‘Metrics: The Path from Cause to Effect. Princeton University Press.\n• Bellemare\, M.F.\, Bloem\, J.R. and Wexler\, N. (2024): The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion. Oxford Bulletin of Economics and Statistics 86: 951-993. https://doi.org/10.1111/obes.12598\n• Didelez\, V. (2025): Causal Reasoning and Inference in Epidemiology. In Ahrens\, W. and Pigeot\, I: Handbook of Epidemiology\, Springer\, New York. https://doi.org/10.1007/978-1-4614-6625-3_74-1\n• Digitale\, J.C.\, Martin\, J.N. and Glymour\, M.M. (2022). Tutorial on directed acyclic graphs. Journal of Clinical Epidemiology\, 142\, pp.264-267.\n• Henningsen\, A.\, Low\, G.\, Wuepper\, D.\, Dalhaus\, T.\, Storm\, H.\, Belay\, D. and Hirsch\, S (2025): Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists. arXiv preprint. https://doi.org/10.48550/arXiv.2508.02310.\n• Hernán\, M.A. (2018). The C-word: scientific euphemisms do not improve causal inference from observational data. American journal of public health\, 108(5)\, pp.616-619.\n• Hernán & Robins (2020): Causal Inference: What If? Chapman & Hall/CRC\, Boca Raton (particularly chapters 1\, 2\, 3\, 4\, 6\, 7\, 8 & 16)\, https://miguelhernan.org/whatifbook\n• Huber\, M. (2023): Causal analysis: Impact evaluation and Causal Machine Learning with applications in R. MIT Press.\n• Huber\, M. (2025): Impact Evaluation in Firms and Organizations: With Applications in R and Python. MIT Press.\n• Morgan\, S.L. and Winship\, C. (2014)\, Counterfactuals and Causal Inference: Methods and Principles for Social Research\, 2nd ed. Cambridge University Press.\n• Tennant\, P.W.\, Murray\, E.J.\, Arnold\, K.F.\, Berrie\, L.\, Fox\, M.P.\, Gadd\, S.C.\, Harrison\, W.J.\, Keeble\, C.\, Ranker\, L.R.\, Textor\, J. and Tomova\, G.D. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International journal of epidemiology\, 50(2)\, pp.620-632.\n• Textor\, J. (2015)\, Drawing and analyzing causal DAGs with DAGitty. arXiv preprint arXiv:1508.04633 \nCourse coordinator\nArne Henningsen\, Associate Professor\, arne@ifro.ku.dk \nTeachers\nArne Henningsen\, Associate Professor\, arne@ifro.ku.dk\nBo Markussen\, bomar@math.ku.dk\nChristine Winther Bang\, cwb@math.ku.dk \nGuest Lecturers\nWhen we taught the course in 2025\, we invited two renowned experts to give guest lectures on two of the methods covered in this course\, respectively. As this worked very well\, we plan to include the same or similar guest lectures in the 2026 course as well. \nDates\n4-8 May 2026 – 9-17 all days. \nExpected frequency\nOnce per year in teaching block 4. \nCourse location\nUCPH Campus \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/estimating-causal-effects-with-observational-data/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260428 DTEND;VALUE=DATE:20260527 DTSTAMP:20260309T141351Z CREATED:20260204T133452Z LAST-MODIFIED:20260309T141351Z UID:10001734-1777334400-1779839999@ddsa.dk SUMMARY:Machine Learning for SCIENCE (MLS) DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe Machine Learning for SCIENCE (MLS) course introduces key analysis methods in Machine Learning. These methods allow investigations of scientific data from most fields\, including data from physical measurements\, questionnaires\, pictures\, internet searches\, satellites\, and biochemical analyses. We cover data cleaning (e.g. missing data\, denoising)\, feature extraction\, machine learning basics (labels\, variables\, parameter optimization\, overfitting\, cross-validation)\, key machine learning and image analysis methods based on both unsupervised and supervised learning\, and visualization. Method-wise\, we start at Linear Discriminant Analysis and end with Deep Learning.\nAt the end of the course\, the students must write a report with a suggestion for an analysis ideally performed on their own research data including a small implementation of a key concept. This report could form the basis for the Data Science Projects PhD course also offered by the Data Science Lab. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge\n• Understand key machine learning concepts (e.g. parameter training\, overfitting).\n• Understand key machine learning methods (e.g. LDA\, supervised learning).\n• Understand key data analysis methods (e.g. feature extraction). \nSkills\n• Develop/adapt/extend a computer-based software method for analysis of relevant data. \nCompetences\n• Propose relevant analysis methods for scientific data science problems.\n• Consider cross-disciplinary data science methods in their research. \nTarget Group\nPhD students from all SCIENCE departments with an element of data science in their research project. \nRecommended Academic Qualifications\nWe use Python for the examples and exercises\, so a basic level of Python programming experience is needed.\nThe Python skills could come from the Python for SCIENCE PhD toolbox course. \nResearch Area\nAll SCIENCE research fields\, and secondarily other scientific fields with a data science element (e.g. health sciences). \nTeaching and Learning Methods\nThe course is composed of sessions combining lectures and exercises.\nFor each topic\, the students will get hands-on experience in applying\, modifying\, and programming analysis methods.\nThe programming examples will be implemented using Python in JupyterLab notebooks. \nType of Assessment\nThe students need to hand in their reports (10 days after the final course day) that must be approved. The students are allowed to work in 2-person groups. \nLiterature\nCourse lecture slides and exercises.\nWe will use data\, examples\, and other material from publicly available sources. \nCourse coordinator\nErik Dam\, Professor\, erikdam@di.ku.dk \nDates\n2026: Apr 28\, May 5\, May 12\, May 19\, May 26. \nCourse location\nPhysically on campus.\nTypically\, at Nørre Campus\, alternatively at Frederiksberg Campus. \nCourse fee\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3600 DKK\n• PhD student from foreign university: 3600 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3600 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3600 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 10.080 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/machine-learning-for-science-mls-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260420 DTEND;VALUE=DATE:20260425 DTSTAMP:20260309T135237Z CREATED:20260119T134442Z LAST-MODIFIED:20260309T135237Z UID:10001855-1776643200-1777075199@ddsa.dk SUMMARY:Numerical Optimization DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved for PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nNumerical optimization is a key computer tool across various fields\, including image processing\, machine learning\, bioinformatics\, economics\, etc. It addresses diverse problems\, Maximum Likelihood or Maximum a Posteriori parameter estimation\, inverse kinematics in robotics and many optimization problems in imaging\, denoising\, segmentation\, reconstruction etc. for instance in medical imaging. \nThis course will equip PhD students with a set of numerical optimization techniques\, making it an excellent addition for those from various scientific backgrounds. It covers the fundamental theory and practical implementation of these methods\, emphasizing deep understanding\, mathematical derivation\, and programming best practices. Students will also be trained on practical examples from the research directions pursued in the IMAGE section. \nLearning outcomes \nKnowledge:\n1. Line search Gradient descent and Newton Method\, Trust Regions\, Gauss-Newton\, Levenberg-Marquardt\, simple constrained optimization including linear programming (simplex and interior point methods) etc. \nSkills:\n2. Ability to use numerical optimization solutions in practice\n3. Ability to use optimization toolboxes such as Python SciPy optimisation packages as well as others. \nCompetences:\n4. Identify practical situations where numerical optimisation is needed.\n5. Ability to formulate a problem as a numerical optimisation task.\n6. Ability to choose a suitable optimization method \nTarget Group\nPh.D. students in computer science\, mathematics\, chemistry\, economics and physics \nRecommended Academic Qualifications\nM.Sc in computer science\, mathematics\, chemistry\, economics and physics or equivalent. \nResearch Area\nComputer Science\, mathematics\, chemistry\, economics and physics. \nTeaching and Learning Method\n5 full days with morning lecture and afternoon exercises \nType of Assessment\nOne or two large take home assignments. \nLiterature\nNumerical Optimization\, J. Nocedal and S. J. Wright. Springer. Course Notes. \nCourse coordinator\nFrançois Lauze\, Associate Professor\, DIKU. \nGuest Lecturer\nBernhard Kerbl\, Assistant Professor\, DIKU \nDates\nBlock 4\, April 20-24\, 2026 \nExpected frequency\nOnce a year\, block 4 unless there is enough interest from students\, then we will run a new occurrence in the beginning of block 2. \nCourse location\nNorth Campus or Frederiksberg Campus \nRegistration\nRegistration with waiting list \nDeadline for registration\n4 weeks before course starts. If seats are available late registration might be accepted (with a cap on 30 participants) \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/numerical-optimization/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260420 DTEND;VALUE=DATE:20260425 DTSTAMP:20260309T135225Z CREATED:20260119T122748Z LAST-MODIFIED:20260309T135225Z UID:10001731-1776643200-1777075199@ddsa.dk SUMMARY:International School of Chemometrics - BASICS DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved to other applicants. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe ISC-2026 is a four-week school designed to introduce different key aspects of Data Science and Machine Learning in different branches of science (chemistry\, food & feed\, physics\, environmental\, political economics\, etc).\nIt is addressed to BSc\, MSc\, PhD students/post-docs\, professors\, as well as industrial and private researchers.\nIMPORTANT: The ISC-2026 is structured in FOUR different and independent modules: PROGAMMING\, BASICS\, INTERMEDIATE\, CHALLENGES.\nThe students CAN CHOOSE WHICH ONES TO DO.\nPlease make sure to register individually for each course you intend to participate in. \nBASICS MODULE: \nA basic introduction to Chemometrics\, data types\, data pre-processing\, PCA\, Multivariate Linear Regression\, and Linear Algebra\nThis seminar contains several general topics:\n– PCA – PREPRO – REGRESS: Data exploration and regression. Principal Component Analysis has become the most powerful and versatile tool for exploring data tables in Analytical Sciences. Here\, we present a course to illustrate the main benefits and drawbacks of PCA when applied to various types of analytical data\, including spectroscopy\, environmental assessment\, sensory experiments\, performance experiments\, and chromatography. Moreover\, the preprocessing of different types of data will also be addressed in the seminar as a prerequisite for exploring the data optimally. If PCA is the keystone of pattern recognition methods\, PLS is the keystone of multivariate calibration methods. This seminar will give a general overview of different multivariate calibration strategies (Multilinear Regression\, Principal Component Regression)\, and will focus on Partial Least Squares regression.\n– LinAl: Linear Algebra. The Foundation for chemometric modelling is Linear Algebra. Why do the algorithms work? Why are the models meaningful? A math-derived answer to these questions can be found using linear algebra. The seminar will focus on hands-on experience with some fundamental linear algebra concepts\, including rank\, determinant\, inverse\, pseudo inverse\, eigenvalues\, singular value decomposition\, orthogonality and basis sets. We will analyze a few real-life datasets\, but the purpose of the seminar is to be a proficient mechanic unravelling the black box of algorithms and models\, while other courses will teach you how to drive a car. \nImportant: Refer to the detailed calendar for additional information. The modules are not divisible. Therefore\, the entire week counts as a single course. \nPrevious knowledge needed: None \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with:\n– Matlab\n– PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO\, a fully functional demo will be available for the School. \nTeachers: José Manuel Amigo\, Beatriz Quintanilla\, Morten A. Rasmussen. See guest lecturers for further information. \nLearning outcomes\nIntended learning outcome for the students who complete the ISC-2026 complete course: \nKnowledge\n• Learn the basics of data analysis methods.\n• Learn to handle data and create proper datasets and libraries for further analysis\n• Learn critical thinking regarding Machine Learning\, Chemometrics and IA \nSkills\n• Develop their own data analysis protocols\n• Code basic algorithms and the resources available for data analysis\n• Apply the acquired knowledge to any problem related to their own research \nCompetences\n• Understand the structure of a vast number of data types and the issues derived from the data\n• Independent thinking for the solution of their problems\n• Interaction with other peers and teachers \nTarget Group\nThe course is specifically addressed to PhD students.\nAdditionally\, the course attracts a high number of BSc\, MSc\, postdoctoral researchers\, and professors.\nAnother relevant audience is Industry. The course receives students from 2 to 3 companies every year. \nRecommended Academic Qualifications\nNone specifically required. We start from basic topics and go all the way to a more advanced topics. \nResearch Area\nChemometrics\, machine learning\, spectroscopy\, artificial intelligence\, programming\, statistics \nTeaching and Learning Methods\nThe seminars of the International School of Chemometrics will comprise a mix of presentations from world-leading researchers\, combined with practical and theoretical exercises in data analytics software\, which will provide students with hands-on experience in applying the tools taught. The exercises are done under the supervision of the teachers.\nThe initial week of programming offers instruction in three different languages (MATLAB\, R and Python)\, and all the instruction in this part is based on e-learning. The remaining three weeks of the school are dedicated to physical on-site training. \nType of Assessment\nThe course is completed by attending and development during the practical exercises will be evaluated by interest of the student. \nLiterature\nPeer-reviewed papers provided during the course. \nCourse coordinator\nRasmus Bro\, Professor\, rb@food.ku.dk \nGuest Lecturers\n– Prof. Rasmus Bro\, University of Copenhagen. Main coordinator. Teacher in CHALLENGES (Multiway and GLUE).\n– Prof. José Amigo Rubio\, University of the Basque Country. Primary person responsible for day-to-day business operations throughout the entire School. Teacher in PROGRAMMING (MATLAB)\, BASICS and CHALLENGES (GLUE).\n– Assistant Prof. Beatriz Quintanilla\, University of Copenhagen. Primary person responsible for day-to-day business operations throughout the entire School. Teacher in BASICS and CHALLENGES (Multiway and GLUE).\n– Prof. Morten A. Rasmussen\, University of Copenhagen. Teacher in BASICS (LinAl).\n– Assoc. Prof. Asmund Rinnan\, University of Copenhagen. Teacher in INTERMEDIATE (VarSel).\n– Assoc. Prof. Agnieszka Smolinska\, Maastricht University. Teacher in INTERMEDIATE (DoE-ASCA).\n– Prof. Davide Ballabio\, University of Milano-Bicocca. Teacher in INTERMEDIATE (CLASS).\n– Prof. Anna de Juan\, University of Barcelona. Teacher in CHALLENGES (MCR).\n– Dr. Neal Galhaguer\, Eigenvector Research. Teacher in CHALLENGES (HYPER).\n– Dr. Carlos de Cos\, The Mathworks. Teacher in CHALLENGES (NonLin).\n– Assoc. Prof. Sergey Kucheryavskiy\, University of Aalbrog. Teacher in PROGRAMMING (R).\n– Dr. Anders Krogh Mortensen\, The AI Lab. Teacher in PROGRAMMING (Python).\n– Prof. Federico Marini\, University of Rome La Sapienza. Teacher in CHALLENGES (GLUE). \nDates\nPROGRAMMING: 13th April – 17th April\, 2026.\nBASICS: 20th April – 24th April\, 2026\nINTERMEDIATE: 25th April – 1st May\, 2026\nCHALLENGES: 4th May – 8th May\, 2026 \nDetailed calendar\nISC-2026 \nWeek 01 – Online PROGRAMMING\n13-april 14-april 15-April 16-april 17-april\nProgramming Programming Programming Programming Programming \nWeek 02 – BASIC\n20-april 21-april 22-april 23-april 24-april\nPCA LinAl PREPO REG REG \nWeek 03 – INTERMEDIATE\n25-april 26-april 27-april 28-april 01-may\nVARSEL VARSEL CLASS CLASS DoE – ASCA \nWeek 04 – CHALLENGES\n04-may 05-may 06-may 07-may 08-may\nMCR MCR NonLin NonLin GLUE – 1000M\nHYPER HYPER MULTIWAY MULTIWAY \nCourse location\nFrederiksberg Campus \nCourse fee\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant type – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/international-school-of-chemometrics-basics/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260413 DTEND;VALUE=DATE:20260418 DTSTAMP:20260309T134618Z CREATED:20260119T122042Z LAST-MODIFIED:20260309T134618Z UID:10001767-1776038400-1776470399@ddsa.dk SUMMARY:The Anthropological Theory of Didactics in Mathematics & Data Science Education DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved to other applicants. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nSpecial rules apply for this course\nPlease note that all applicants will be placed on the waiting list upon registration.\nAfter registering for the course\, please send an email to the course coordinator Britta Eyrich Jessen (britta.jessen@ind.ku.dk) latest on 28 January 2026.\nThe email must contain your CV and a short description of your PhD project.\nAbout one week after registration deadline\, all applicants on the waiting list will be notified \nAim and Content\nThe course will provide a broad introduction to the anthropological theory of the didactic (ATD) as a framework for research in mathematics and data science education\, in particular didactic transposition theory\, praxeological analysis\, Herbartian schemas and Didactical Engineering based on Study and Research Paths. Moreover\, applications and methods in mathematics and data science education from primary to tertiary education will be provided and discussed. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\n• Acquire knowledge regarding the recent advances in the theoretical framework and methods of current ATD research\n• Gain knowledge from central texts of the ATD based literature in mathematics education (in English) \nSkills:\n• Develop the ability to recognize and validate problems within this framework\n• Apply relevant knowledge and methods when addressing the identified problems\n• \nCompetences:\n• Gain competence for and experience with writing scientific\, peer-reviewed manuscripts (that can be developed further into publications at the participants’ own initiative).\n• \nTarget Group\nPhD students in mathematics or data science education at all levels of education. \nRecommended Academic Qualifications\nTo be enrolled as PhD student with a PhD project within mathematics or data science education from primary to tertiary level education. \nResearch Area\nDidactics of mathematics (and statistics education) \nTeaching and Learning Methods\nThe course will consist of lectures followed by discussion seminars linked to each set of lectures. These will be provided in connection with participants’ presentation of their 5 pages papers applying the course literature to their own projects. \nType of Assessment\nBy February 15\, participants must submit a 5-page synopsis of their own research and how they propose to use the theoretical framework in their work (based on their own readings prior to the course)\nDuring the presence part of the course in Copenhagen\, from April 13\, 2026 9AM to April 17 at 3PM\, participants must actively participate to develop their ideas under the supervision of the course teachers and guest lecturers. This includes giving a 20 min presentation of their 5-page synopsis\nBy May 15\, participants must submit a 10-page paper on the use of the theoretical framework in their own research. \nLiterature\nThe course will be based (in part) by selected chapter from:\nY. Chevallard\, B. Barquero\, M. Bosch\, I. Florensa\, J. Gascón\, P.Nicolás\, and N. Ruiz-Munzón (Eds.\, 2022): Advances in the Anthropological Theory of the Didactic. Birkäuser Cham\nAnd\nIgnasi Florensa\, Noemí Ruiz-Munzón\, Kristina Markulin\, Berta Barquero\, Marianna Bosch\, Yves Chevallard (2024): Extended Abstracts – Proceedings of the 7th International Conference on the Anthropological Theory of the Didactic (CITAD7). Birkhäuser\, Cham. \nCourse coordinator\nAssociate professor\, Britta Eyrich Jessen \nGuest Lecturers\nThe following guest lecturers will hold lectures\, provide feedback on students’ papers and guide the discussions of their presentations during the course. The workload will be divided equally among the guest lecturers.\nProfessor Marianna Bosch (University of Barcelona\, Spain)\nProfessor Alejandro González-Martín (Université de Montréal\, Canada)\nProfessor Heidi Strømskag (Norwegian University of Science and Technology\, Norway)\nFurthermore\, the professor Carl Winsløw\, Department of Science Education\, University of Copenhagen\, be co-teaching and running the course in collaboration with the course coordinator. \nDates\nApril 13 – April 17 2026 \nExpected frequency\nNot recurrent \nCourse location\nNiels Bohr Building\, Jagtvej 155A\, 2200 Copenhagen N\, \nCourse fee\n• Participant fee: 2000 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 6000 DKK\n• PhD student from foreign university: 6000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 6000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 6000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 16.800 \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/__trashed-137/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260302 DTEND;VALUE=DATE:20260321 DTSTAMP:20260205T124805Z CREATED:20260115T114910Z LAST-MODIFIED:20260205T124805Z UID:10001867-1772409600-1774051199@ddsa.dk SUMMARY:Scientific Curation of Natural History Collections DESCRIPTION:Enrolment guidelines \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe aim is to offer PhD students at the Science Faculty knowledge\, skills and an understanding of the role of scientific curators as well as both practical and theoretical competences for performing scientific curation and managing on natural history collections.\nNatural History collections represent national and international heritage and are actively used for research\, education and communication. In addition to ensuring access to collections and their metadata for international researchers and society\, scientific curators oversee the strategic management and development of the collections. In this course\, we open the treasure chest and introduce you to all the different aspects and possibilities of 21st century natural history collections. \nThe natural history collections of the Natural History Museum Denmark are one of the oldest and largest in the World\, with more than 14 million specimens of plants\, animals\, fossils and rocks\, representing over 400 years of global fieldwork and collection history. The Botanical Garden of the Museum includes over 8.000 living plant species from all over the World as well as a conservation seed bank. \nNatural history collections serve as repositories documenting the distribution of organisms across time and space\, essentially acting as a time capsule. At the same time\, collections\, both living and preserved\, are an immense source of big data for a wide range of research applications from the core discipline of taxonomy to testing evolutionary relationships\, drivers of biodiversity\, and societal challenges including the current climate and biodiversity crisis. \nThe course includes the following themes:\n• General collection management from field expeditions to the collections or garden. From fieldwork planning and ethical considerations\, international collaboration and guidelines to preservation\, identification\, curation\, registration and open access.\n• Strategic development of collections including identification of priority areas for new collecting and exchange with other museums and gardens as well as improving identifications and metadata.\n• Meta-curation of collections across institutions\n• Digitization and imaging of collections\n• Challenges and opportunities of using public databases such as GBIF for research and inventories.\n• Type specimens as the fundamental basis of a species name\, species circumscriptions\, and formal description of a new species.\n• Cultural history of collections.\n• Collection based research across time\, space and species including changes in phenology\, distribution\, invasive species and endangered species.\n• Use of collections to address societal challenges including conservation.\n• Genomic work with historical collections and challenges related to ancient DNA.\n• Communicating collections through exhibitions\, social media\, popular engagement and citizen science.\n• Fund raising for collections and research projects. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\n• Basic understanding of curatorial tasks and considerations related to preserve\, develop\, and communicate natural history collections.\n• Basic understanding of the taxonomic hierarchy\, species concepts\, taxonomical nomenclature\, typification and species description.\n• Understand and describe the cultural\, historical and scientific value of natural history collections and their metadata.\n• Identify and communicate the potential of collections for addressing fundamental science questions and societal challenges to diverse audiences.\n• Assess research and exhibition potentials of the collections.\n• Identify initiatives to make the collections more accessible and of use to the broader public. \nSkills:\n• Responsible and sustainable handling of collections and their metadata.\n• Outline strategy\, development\, and management plans for collections.\n• Describe examples of collection-based research and the use of collections to address societal challenges.\n• Design experiments to investigate collection-based research questions.\n• Communicate the cultural and scientific value of natural history collections. \nCompetences:\n• Assess the values\, strengths\, weaknesses and risks of natural history collections and suggest strategic priorities for collection development.\n• Describe collection management needs in general terms and prepare guidelines for the scientific curation of collections.\n• Extract\, present\, and critically discuss in detail the objectives\, methods and results of scientific articles about collection-based research.\n• Quality assessment of metadata and cleaning of database output for use in research or inventories.\n• Outline future research and prepare funding proposals.\n• Identify communication potential of collections and prepare communication plans and activities.\n• Present their own work (in oral and written form) at a level approaching the scientific standard.\n• Identify interdisciplinary collaboration potentials.\n• Create synergy between collections\, research\, education and public engagement. \nTarget Group\nOur main target group is PhD students at NHMD as well as closest departments at the Science Faculty: IGN and BIO. We expect the course will also attract students from all across Science and we will advertise it internationally as we know there is a huge interest in this course from our international museum and university partners. The course is intended as an obligatory introduction to collections curation for PhD students at NHMD as a basis for the PhD School of Science requirement of duty hours. \nRecommended Academic Qualifications\nBasic knowledge of organismal biology and/or geology\, the taxonomic hierarchy\, species concepts\, phylogeny\, microscopy and DNA work is recommended. \nResearch Area\nCuration\, Taxonomy\, Systematics\, Botany\, Zoology\, Geology\, Palaeontology and Digital Sciences \nTeaching and Learning Methods\nThe course is based on actively working with the museum’s natural history collections and consists of a mixture of lectures\, research presentations from museum staff\, hands-on work in the collections\, project work\, and communication/public engagement activities. During the course students work together on selected projects on a part of the collections. All projects include identifying strategic priority areas\, making action plans for improving the value and use of a part of the collection\, writing a simple grant application and communication plan\, and actively communicating a story or aspect of the collections to a public audience. A labwork component may be included depending on interest of the course participants. \nType of Assessment\nThe students will hand in a 5-page assignment which will be either a collection policy\, strategy or a funding application aimed at attracting external funding for developing a curatorial area. \nLiterature\nWill be provided prior to the course \nCourse coordinator\nProfessor Anders P. Tøttrup\, aptottrup@snm.ku.dk \nGuest Lecturers\nAdd information on the guest lecturers\, including affiliated institution and their contribution to the course \nDates\n2-20 March 2026\nTeaching will occur 2 – 13 March 2026\nHand-in assignment 16 – 20 March 2026 \nExpected frequency\nThe course will run annually. \nCourse location\nThe course will mainly run from the Zoological Museum Building\, Universitetsparken 15\, but teaching will also take place on several other of the museum addresses \nRequirements for signing up\nWith registration we ask for ½-1 page letter stating taxonomic research area and interest.\nPlease send this to: Professor Anders P. Tøttrup\, aptottrup@snm.ku.dk \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 6000 DKK\n• PhD student from foreign university: 6000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 6000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 6000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 16.800 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/__trashed-119/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260202 DTEND;VALUE=DATE:20260207 DTSTAMP:20251112T115259Z CREATED:20251112T115259Z LAST-MODIFIED:20251112T115259Z UID:10001711-1769990400-1770422399@ddsa.dk SUMMARY:Large-Scale Data Analysis with R: Transcriptomics and Metabolomics DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nMany experimental procedures such as the various “-omics” techniques routinely employed within biotechnology/biological research fields produce vast amounts of data. Therefore\, the amount of available data in many biological disciplines is steadily increasing. Fundamental knowledge and skills of large-scale computing systems and analysis methods is required to make use of this wealth of information. The purpose of this course is to introduce the theory and practice of large-scale data analysis to students\, which will allow them to perform and assess different types of ”-omics”-scale data procedures\, specifically focusing on Transcriptomic data (RNAseq) and Metabolomic data (LC-MS). \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge\n• The general principles of large-scale data analysis\n• Common pitfalls in large-scale data analysis\n• The basic concepts underlying clustering and visualization techniques \nSkills\n• How to efficiently keep\, move\, and analyse large amounts of data\n• How to structure and perform large-scale data analyses in a coding-based software environment\, such as for example R\n• Handling and modifying large datasets\n• Visualization and dissemination of data \nCompetences\n• Analysing different types of large-scale biotechnology data\n• Critically evaluating the quality of different types of biotechnology data\n• Assessing and understanding results of large-scale data analyses \nTarget Group\nAll PhD-students within biology\, biotechnology\, medicine\, pharmaceutical sciences etc. \nRecommended Academic Qualifications\nBasic statistical understanding equivalent to a MSc from SCIENCE; Beginners level experience with R \nResearch Area\nLarge-scale data analysis of “-omics” data in the biological sciences. \nTeaching and Learning Methods\nLectures and computer exercises \nType of Assessment\nCompletion of the course will rely on the production and acceptance of a complete data analysis report in Rmarkdown. \nLiterature\nOriginal literature\, software manuals and tutorials\, and teacher provided compendia \nCourse coordinator\nHenrik de Fine Licht\, Associate Professor\, hhdefinelicht@plen.ku.dk\nMeike Burow\, Professor\, mbu@plen.ku.dk \nDates\n02-06 February 2026 \nCourse location\nPLEN – Frederiksberg Campus \nRequirements for signing up\nThis course is for PhD-students. If you are not a PhD student\, please contact the course organizers before signing up. \nCourse fee\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/large-scale-data-analysis-with-r-transcriptomics-and-metabolomics-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260126 DTEND;VALUE=DATE:20260131 DTSTAMP:20251112T115123Z CREATED:20240821T085138Z LAST-MODIFIED:20251112T115123Z UID:10001346-1769385600-1769817599@ddsa.dk SUMMARY:Fundamentals of the PhD education at SCIENCE - module 2 -K8 DESCRIPTION:Enrolment guidelines \nThe module is mandatory for all new PhD students enrolled at Science from 1 January 2024 as part of the course ‘Fundamentals of the PhD education at SCIENCE’. \nIf you are a double degree student with a foreign host\, you can not enroll in this course. \nLearning objectives\nThe learning outcomes for the segments are given below. Each outcome is marked K for Knowledge\, S for Skills\, or C for Competences. \nData Management segment:\n• Identify relevant legislation\, requirements\, and policies on data management applicable to research projects at UCPH (K).\n• Recognize recommendations and requirements regarding open and reproducible research designs\, data collection\, and data publication (K).\n• Classify data and conduct a risk assessment to ensure the secure storage of data (S).\n• Assess how data can be preserved and shared to guarantee FAIR use of data (S).\n• Assess when to use electronic lab notes (S).\n• Contribute to planning and conducting appropriate data and materials management in all phases of their PhD project (C).\n• Be able to adhere to best practices of open and reproducible research (C).\nCareer Management segment:\n• Differentiate typical career paths for SCIENCE PhDs (K).\n• Describe selected understandings of motivation (K).\n• Explore and explain their career priorities (S).\n• Build a professional profile on LinkedIn (S).\n• Assess how different uses of their change of scientific environment may impact their career (S).\n• Integrate knowledge of typical career paths for SCIENCE PhDs with an understanding of their personal career priorities (C).\n• Develop a personal networking strategy and build a professional network that supports their career interests (C).\nData Science segment:\n• Know Data Science as a research methodology (K).\n• Apply basic Statistics or Machine Learning computational frameworks and methods when appropriate in their research (S).\n• Build a network for potential inter-disciplinary data science collaborations (C). \nContent\nThe purpose of the course is to introduce the students into Data Science\, Data Management\, and Career Management:\n• Many students will apply/develop Data Science methods (data analysis\, statistics\, and machine learning) directly in their own research; and all students should be aware of this potential.\n• Some students will directly apply Data Management principles\, and all students should be aware of the general policies.\n• All students should actively manage their careers. \nThe module consists of 10 morning/afternoon sessions during the on-campus course week (2 on Data Management\, 1 on Career Management\, and 7 on either Machine Learning or Statistics). Prior to course start\, the students will choose to follow either the Statistics or Machine Learning variant of the module. \nThe aim of the Data Management segment is to equip PhD students with knowledge and skills to:\n• Manage data and primary materials responsibly during their PhD projects.\n• Create open and reproducible research outputs. \nThe aim of the Career Management segment is that PhD students start to explore their values\, motivation\, and the great variety of career options that are open to them after their PhD. The segment will cover:\n• Megatrends in the labour market and typical career paths for PhDs from the natural sciences.\n• Motivation\, values\, and career priorities.\n• Change of scientific environment.\n• Networking for career development. \nThe Data Science segment will aim ensure that PhD students will consider Data Science as a methodology and allow them to apply basic Statistics or Machine Learning methods when appropriate in their research. \nThe Statistics variant will cover:\n• Statistical thinking and methodology\, including statistical power and principles for experimental design.\n• Statistical modelling using fixed and random effects.\n• Tabular and graphical presentation of experimental results and statistical analyses.\n• The R programming environment for Statistics and basic comparisons to other statistical software. \nThe Machine Learning variant will cover:\n• Machine learning foundations and methods.\n• Data Science caveats and best practice.\n• Introduction into AI with focus on intuitive understanding the big models in terms of potential\, applicability\, limitations\, and sustainability.\n• Application of AI and machine learning in science. \nParticipants\nAll PhD students at SCIENCE as part of the PhD Fundamentals course. Module 2 is about 6 months into the PhD programme. \nLanguage\nEnglish \nForm\nThe module will include a mixture of lectures\, exercises\, plenary and group discussions\, and peer feedback during the on-campus segments. Around the on-campus week\, the students will perform eLearning as well as reflective and practical assignments. \n Type of assessment\nAll segments require presence and active participation.\nThe students must hand in a report where they choose between a Data Management Plan\, or a report related to their Statistics or Machine Learning specialization. \nRegistration\nAutomatic registration when enrolling in the PhD program at SCIENCE. \nRemarks\nThe PhD School at the Faculty of SCIENCE is committed to building a learning environment that welcomes\, includes\, and empowers all its PhD students. By building a Faculty-wide peer community of PhD-students with the Fundamentals course\, we secure that all PhD candidates are given adequate instruction in a range of essential competences that lie outside the core scientific research skills offered through supervision\, tool-box and specialized PhD courses. Moreover\, we build bridges between different research programmes at the Faculty of SCIENCE and offer diverse\, multidisciplinary fora of exchange strengthening the PhD candidates’ scientific and social networks and laying the foundation for a strong alumni culture. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/fundamentals-of-the-phd-education-at-science-module-2-k8/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260121 DTEND;VALUE=DATE:20260131 DTSTAMP:20251112T115008Z CREATED:20251112T115008Z LAST-MODIFIED:20251112T115008Z UID:10001701-1768953600-1769817599@ddsa.dk SUMMARY:Python for SCIENCE DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% og the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nThe course is free of charge for PhD students at Danish universities (except CBS). \nAll other participants must pay the course fee (except if you are a master’s student from a Danish University). \nAim and Content\nThe course introduces to the dominant programming language in data science\, Python. Python is a general-purpose programming language that is currently being used in many active data science projects with open-source libraries available.\nThe workshop will teach the basic programming constructs in Python and then provide data science examples\, including data import\, visualization\, and analysis. We will introduce integrated development interfaces such as JupyterLab. We will introduce libraries from active open-source frameworks (numpy\, pandas\, matplotlib\, sklearn\, …). We will further discuss methods for securing reproducibility of research results (code architecture\, versioning\, open source).\nThe course is aimed at PhD students\, who need tools for data exploration\, data analysis\, and data visualization. Post Docs\, Professors\, and Master’s thesis students from SCIENCE may register for participation and will be accepted if space permits. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge\n• Understand computational thinking concepts.\n• Understand key programming elements (e.g. variables\, objects\, functions\, modules).\n• Know useful open-source libraries (e.g. pandas\, matplotlib\, sklearn). \nSkills\n• Develop/adapt/extend a computer-based software program for analysis of relevant data.\n• Apply good practice co-development principles. \nCompetences\n• Propose relevant analysis methods for scientific data science problems.\n• Consider cross-disciplinary data science methods in their research. \nTarget Group\nPhD students from all SCIENCE departments with an element of data science in their research project. \nRecommended Academic Qualifications\nNone. \nResearch Area\nAll SCIENCE research fields\, and secondarily other scientific fields with a data science element (e.g. health sciences). \nTeaching and Learning Methods\nThe course is composed of sessions combining lectures and exercises.\nFor each topic\, the students will get hands-on experience in applying\, modifying\, and creating elements of analysis methods.\nThe programming examples will be implemented in JupyterLab notebooks and in pure Python source files. \nType of Assessment\nThe students need to be physically present and active during the course. \nLiterature\nCourse lecture slides and exercises.\nWe will use data\, examples\, and other material from publicly available sources. \nCourse coordinator\nErik Dam\, Professor\, erikdam@di.ku.dk \nDates\nWed 21\, Fri 23\, Mon 26\, Wed 28\, Fri 30 January 2026\, from 9 to 16 all days. \nCourse location\nPhysically on campus.\nTypically at Nørre Campus\,\nalternatively at Frederiksberg Campus. \nSeats to PhD students from other Danish universities will be allocated on a first-come\, first-served basis and according to the applicable rules.\nApplications from other participants will be considered after the deadline for registration. \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/python-for-science-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20251029 DTEND;VALUE=DATE:20251108 DTSTAMP:20250807T124028Z CREATED:20250807T124028Z LAST-MODIFIED:20250807T124028Z UID:10001522-1761696000-1762559999@ddsa.dk SUMMARY:Statistical methods for SCIENCE (SmS) DESCRIPTION:Content \nToolbox course on statistical methodology with focus on choice of statistical models\, practical implementation using statistical software\, and presentation and interpretation of results. For the practical implementation\, we use the state-of-the approach for data analysis in R including data wrangling and visualization via the tidyverse package. The course covers the most common statistical models used in the empirical sciences. Specifically\, the following topics are taught: data types\, data visualization\, table-of-counts\, categorical regression\, linear and multilinear regression\, analysis of variance\, random effects\, hypothesis testing and statistical power\, correction for multiple testing\, estimated marginal means and confidence intervals\, and design of experiments. \nFormal requirements \nThere are no formal requirements. However\, recommended prerequisite is some introductory statistics course during the participant’s bachelor or master studies\, or the PhD school Fundamentals II course. \nLearning outcome \nThe students are introduced to statistical models commonly used in the empirical sciences for univariate end-points. The statistical methodology is discussed with emphasis on how models are used and interpreted\, and the students are trained to do the statistical analyses using the statistical software R. \nAfter course completion\, the students should be able to: \nKnowledge:\n• Understand elements of frequentist statistics including estimation\, confidence intervals\, hypothesis tests\, model validation.\n• Understand data types and organization in tidy data.\n• Understand assumptions for statistical analyses.\n• Understand concepts of fixed and random effects.\n• Understand solutions to the multiple testing problem. \nSkills:\n• Identify the data types in a particular dataset\, and choose an adequate statistical model.\n• Make high quality visualizations of data.\n• Report results via the estimated-marginal-means technology.\n• Use R to perform the statistical analysis via the RStudio interface.\n• Use relevant R packages. In particular\, tidyverse\, emmeans\, and lme4. \nCompetences:\n• Formulate scientific questions in terms of statistical hypothesis.\n• Conduct statistical analysis using the discussed models.\n• Interpret the results of a statistical analysis.\n• Critically reflect over the results\, conclusions and limitations of a statistical analysis.\n• Judge when to seek help from a skilled statistician. \nLiterature \n• Chester Ismay\, Albert Y. Kim: “Statistical Inference via Data Science: A ModernDive into R and the Tidyverse”\, CRC Press\, The R Series\, 2019. Book is also available online at www.moderndive.com.\n• Supplementary material on random effects and estimated marginal means.\n• Software R and RStudio is free and may be downloaded from www.r-project.org and www.posit.co. \nTeaching and learning methods \nLectures and exercises including use of computers. In the first half of the course days focus will be on lectures\, and in the second half on individual coursework with exercises. Participants must bring their own laptops with R and RStudio installed. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/statistical-methods-for-science-sms/ LOCATION:Department of Mathematical Sciences Universitetsparken 5 CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250924 DTEND;VALUE=DATE:20251023 DTSTAMP:20250807T122053Z CREATED:20250807T122053Z LAST-MODIFIED:20250807T122053Z UID:10001662-1758672000-1761177599@ddsa.dk SUMMARY:Applied High Performance Computing DESCRIPTION:Aim and content \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% og the seats are reserved to PhD students from other Danish Universities/faculties (except CBS).\nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list.\nThe course is free of charge for PhD students at Danish universities (except CBS).\nAll other participants must pay the course fee (except if you are a master’s student from a Danish University). \nAim\nComputational methods are becoming essential in many areas of science\, and the solution to many problems depend on computers that are vastly faster and holds more memory than what a single high-end server can offer. Top supercomputers consist of up to a billion processor cores working in parallel and new supercomputers are mostly based on GPUs for high performance modelling and AI workloads. Programming such highly parallel computers is difficult\, and ensuring both program correctness and high performance is non-trivial.\nIn this course students will learn how to get high performance from applications\, how to use accelerators (GPUs)\, and how to parallelise programs inside a single server (shared mememory parallelisation) and across many computers (distrbuted memory parallelisation).\nLectures will introduce the theoretical concepts\, and it is put in to practice through hands-on exercises. Students will learn to map algorithms to parallel architectures and how to decompose problems for parallel execution.\nWe will use ERDA to execute the programs on a real high performance computing infrastructure and evaluate both performance\, scalability and correctness of the programs. The hands-on exercises use real-world examples to illustrate different techniques that are well-suited to each parallel architecture.\nDuring the exercises we will each week introduce a new tool to aid in the development of high performing programs.\nWe will use Python\, C++\, and Fortran as the course languages\, and most exercises will be available in all three\, while some will only be avilable in C++ and Fortran. The students can use the language of their choice to complete the exercises. \nDetailed Content\nWeek 1:\nSingle core performance: Memory access\, vectorization\, Structure-of-Arrays vs Arrays-of-Structures\nTools: Performance profiler\, Makefile\, Debugger\nPlatform: ERDA DAG\nExercise: Molecular dynamics [Python\, C++\, Fortran]\nWeek 2:\nData processing: Task farming\, message passing\nTools: SLURM batch system\nPlatform: ERDA MODI SLURM cluster\nExercise: data processing workflows [Python\, C++\, Fortran]\nWeek 3:\nShared memory architecture: Threads\, OpenMP\nTools: Thread checker\nPlatform: ERDA DAG\nExercise: Propagation of a seismic wave [C++\, Fortran]\nWeek 4:\nGPUs: SIMT architecture\, programming for an infinite number of cores\nTools: GPU profiler\nPlatform: ERDA DAG with datacenter GPUs \nWeek 5:\nDistributed memory architecture: MPI\, domain decomposition\nTools: debugging and benchmarking across many computers\nPlatform: ERDA MODI SLURM cluster\nExercise: Flat world climate model [Python\, C++\, Fortran]\nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\nThe students will understand the challenges in addressing parallelization and adaptation to GPUs of applications and limitations of the available hardware. \nSkills:\n• Design and implement parallel applications.\n• Use a SLURM batch system to execute large parallel applications on a supercomputer.\n• Implement simple task farming to scale data analytics applications across many computers for orders of magnitude reduction in time-to-solution.\n• Adapt a program to execute in parallel on a shared memory computer using OpenMP.\n• Parallelize across computers with Message Passing Interface (MPI).\nTransform a program to execute efficiently on a GPUs and understand when it is beneficial. \nCompetences:\nThe overall purpose of this course is to enable the student to write high performance parallel applications on a range of parallel computer architectures and be able to deploy data analytics workloads effortlessly on a supercomputer using a batch system. \nTarget Group\nThe course is aimed at PhD students\, who need to understand how to use GPUs and parallel computing tools to scale their applications and data analytics workflows. \nRecommended Academic Qualifications\nAcademic qualifications equivalent to a MSc degree.\nIt is necessary to have basic programming experience. Having some experience with applications in scientific modelling\, simulation or data-processing is useful. Course languages are Python\, C++ and Fortran. \nResearch Area\nThis course is broadly relevant for students working with large-scale data analysis and modelling both in SCIENCE and in other fields\, such as life sciences\, social sciences and economics. \nTeaching and Learning Methods\nThe course is composed of sessions combining lectures and exercises. Each week a new topic will be introduced. and the students will get hands-on experience in applying\, modifying\, and programming.\nThe student can choose the programming language that is most relevant for them. \nThe generic structure for each week is:\n• Preparation:\no background literature to give an overview and provide reference material\no motivational video explaining the science behind the exercise\no video introduction to the exercise \n• Lecture:\no Covering the theory behind the topic\, the relation and limitations in terms of hardware\, and an introduction to the relevant methods \n• Class instruction:\no Introduction to exercise\no Introduction to relevant tools\no Hands-on help with exercise \n• Exercise:\nO Practical exercise allowing the student to put the theory in practice using the programming language of choice \nType of Assessment\nTo pass the course\, the student must hand-in at least four of the five exercises. Solutions to all exercises and general feedback on the exercises will be provided afterwards. \nLiterature\nLiterature will be provided on the Absalon page \nCourse coordinator\nTroels Haugbølle\, Asssociate Professor\, haugboel@nbi.ku.dk \nDates\nEvery Wednesday 9 – 17 in a 5 week period starting last week in September 2025.\nWeeks 39\, 40\, 41\, 43\, 44. \nCourse location\nTBD \nRegistration \nDeadline for registration: 01 September 2025 \nSeats to PhD students from other Danish universities will be allocated on a first-come\, first-served basis and according to the applicable rules.\nApplications from other participants will be considered after the deadline for registration. \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant type – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/applied-high-performance-computing/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250915 DTEND;VALUE=DATE:20250920 DTSTAMP:20250807T122701Z CREATED:20250312T113154Z LAST-MODIFIED:20250807T122701Z UID:10001474-1757894400-1758326399@ddsa.dk SUMMARY:Sensory Evaluation and Food Preferences - Classic and Digital Approaches - 5 ECTS - 2025 DESCRIPTION:Content \nAim and content \nThe PhD course is a biennial intensive course for PhD students in need of carrying out sensory or consumer acceptance studies as part of their PhD project. The course introduces the foundations of sensory science and practice of performing sensory tests with trained panels and by consumers including their design and execution and assessing sensory and consumer data. \nIn the 2025 edition of the course\, we include the new possibilities of machine learning methods like those based on Natural Language Programming that can be used to enrich sensory and consumer research. Students will have hands-on practice with R programming. Furthermore\, we will introduce how AI tools can be used in processing of sensory and consumer data. \nThe course consists of intensive course days in Copenhagen in addition to a course report on a sensory or consumer food choice behaviour topic related to PhD studies\, including a perspective section on the role of machine learning on the chosen topic. \nCourse content:\nDay 1 (half day): Course introduction\, the senses and principles of sensory testing\nDay 2: Sensory testing in practice: Descriptive analysis and rapid methods with exercises\nDay 3: Consumer preferences and beyond: theory and practical exercises\nDay 4: Introduction to machine learning and applications of machine learning in sensory research\nDay 5: Machine learning in consumer research\, course wrap-up (half day) \nFormal requirements \nRequirements:\n– PhD students in need of carrying out sensory or consumer acceptance studies as part of their PhD project. Other researchers from academia/industry also accepted if space allows.\n– Short motivation letter (1-2 paragraphs) to explain why they are taking the course and what they expect to get out of the course\n• Knowledge of basic statistics\n• Basic knowledge of R (otherwise we will give suggested resources to review beforehand) \nLearning outcome \nKnowledge:\n• Understand principles of sensory measurements and evaluation methods\n• Understand healthy food preferences in different situational contexts\n• Understand common machine learning concepts\, including associated algorithms and programmatic tools \nSkills:\n• Be able to design sensory experiments\, analyse and interpret sensory results\n• Be able to explore\, regress\, and classify quantitative and natural language data \nCompetences:\n• Can critically assess literature in the fields of sensory and consumer research.\n• Can select appropriate machine learning methodologies for solving research problems in sensory and consumer science.\n• Can carry out appropriate sensory and consumer methodologies in own research project. \nLiterature \nReading materials will be selected for the course by the organisers and the guest lecturers. They will be distributed to the students approximately two weeks before the course. \nTarget group \nPhD students working with sensory or consumer data from all areas\, including but not limited to food science\, nutrition\, biology\, agricultural sciences\, psychology\, neuroscience\, marketing \nTeaching and learning methods \nThe course consists of one or two modules:\nI. Intensive course program in Copenhagen\, Denmark including networking\nII. Individual assignment and preparation of a report (deadline 15 November 2025) \nLecturers \n>Professor Karin Wendin\, Kristianstad University\, Sweden\n>Associate Professor Qian Janice Wang\, University of Copenhagen\n>Associate Professor Sébastien Le\, Agrocampus Ouest\, France\n>Dr. Kevin Kantono IFF\, The Netherlands\n>Professor Lisa Methven University of Reading\, UK\n>Professor Wender Bredie\, University of Copenhagen\, \nRemarks \nThe course fee includes course materials\, coffee/tea during breaks\, lunches and networking dinner. Participants should cover their own expenses of travel and accommodation. \nThe course fee will depend on the affiliation of the participants as follows:\n• PhD students: 2\,000 DKK\n• Postdoctoral university staff/Non-profit: 4\,000 DKK\n• Industry/For-profit: 9\,000 DKK\nFull refund if cancellation latest two weeks before course start. \nCo-organisers are Professors Wender Bredie and Karin Wendin \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/sensory-evaluation-and-food-preferences-classic-and-digital-approaches-5-ects-2025/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250825 DTEND;VALUE=DATE:20250830 DTSTAMP:20250505T124927Z CREATED:20250505T124927Z LAST-MODIFIED:20250505T124927Z UID:10001603-1756080000-1756511999@ddsa.dk SUMMARY:Methods for Statistical Evaluation of AI DESCRIPTION:Aim and content \nThe aim of course is to equip the participants with statistical methods\, and knowledge of the newest research within statistical methods\, for evaluation of machine learning and AI. \nThe course runs as a summer school\, retreat for five full days. As preparation\, the students prepare a poster framing their own research in relation to the theme of the summer school. This includes reading selected papers as preparation before the summer school. The posters will be presented by the students at a poster session the first evening of the summer school. The posters are used for group work and further refinement through an assignment\, which finalizes the course. \nThe five course days exists of lectures\, including smaller assignments and discussion to activate the students during lectures. The lecturers consist of both external lecturers as well as lecturers from Danish Universities. Group work is also carried out during the five days of summer school. An individual assignment is handed in one week after the summer school. \nFormal requirements \nThe students need qualifications in statistical methods and machine learning or in one of these areas in depth. \nLearning outcome \nKnowledge:\n• Understand a variety of statistical methods\, including PAC bounds\, measures for evaluation of AI\, and fairness metrics\, fairness calibration and their short comings. \nSkills:\n• Choose between a selection of statistical methods for evaluation of ML and AI\n• Assess fairness of ML models\n• Calibrate for better fairness\, but also understand when calibration can skew fairness even further\n• Apply different sampling strategies for building AI systems\, depending on the requirements of the application. \nCompetences:\n• Develop the competence to critically assess AI models\, identifying strengths\, weaknesses\, and potential biases in model behaviour.\n• Ability to incorporate fairness considerations withing the design and development of ML\, ensuring that ethical principles guide the creation and deployment of AI systems.\n• Build ability to engage in peer review\, provide constructive feedback\, and contribute to the collective advancement of knowledge in statistical AI evaluation.\n• Promote abilities to identify the gaps and open research challenges in statistical evaluation of AI\, including developing methods for necessary and sufficient methods for evaluation of AI models. \nTeaching and learning methods \nLectures\, group work\, and individual assignments. \nLecturers \nExternal guest lecturers: \n• To be confirmed: Gaël Varoquaux\, INRIA\, INRIA Saclay-Ile-de-France Research Centre\, France :\nCourse (tentative title): Evaluation of Machine Learning models on typical tabular data.\nShort bio: Research direction of the Soda team in INRIA. He is specialized in applications of Machine Learning to health. Prof. Varoquaux is a co-founder of the Scikit-learn library that is a widely used Machine Learning library in Python. \n• Benjamin Guedj\, Department of Computer Science\, University College London\, United Kingdom:\nTitle: Artificial intelligence from a statistical perspective.\nShort bio: Dr. Benjamin Guedj is specialized in machine learning. He is a Research Fellow at University College London (UCL) in the Department of Computer Science and a research scientist at Inria\, France. His research focuses on theoretical machine learning\, including statistical learning theory\, PAC-Bayes\, and generalization for deep learning. \n• To be confirmed: Jose Hernandez-Orallo\, Valencian Research Institute for Artificial Intelligence\, Universitat Politècnica de València\, Spain:\nCourse (tentative title): Evaluation of cognitive capabilities of AI systems.\nShort bio: The main research of Prof. Hernández-Orallo focuses on AI evaluation\, benchmarking for measuring AI capabilities\, and the safety of AI systems. \nGuest lecturers from Danish institutions: \n– To be confirmed:  Prof. Aasa Feragen\, Image Analysis and Computer Graphics\, DTU-Compute\, Technical University of Denmark.\nCourse (tentative title): Geometric Statistics in Image Analysis. \n– To be confirmed: Prof. Christian Igel\, Department of Computer Science\, University of Copenhagen\, Denmark.\nCourse (tentative title): PAC-Bayesian Analysis of Ensemble Machine Learning Models. \n– Prof. Line Clemmensen\, Dept. of Mathematical Sciences\, University of Copenhagen and DTU-Compute\, Technical University of Denmark.\nTitle: Data representativity for Machine Learning and AI systems. \n– Prof. Murat Külahci\, Statistics and Data Analysis\, DTU-Compute\, Technical University of Denmark.\nCourse (tentative title): Active learning in time-series and sequential data. \n– Assoc. Prof. Andres Masegosa. Department of Computer Science at Aalborg University\, Technical University of Denmark.\nCourse (tentative title): Generalization of Deep Neural Networks. \nRemarks \nThe participant fee is 5\,600 DKK and covers five days incl overnight stays\, food\, social event\, and all course content. \nIt is co-hosted with D3A. \nHow to apply:\nPhD students enrolled at University of Copenhagen (UCPH)\n1) Please provide us with “stedkode” and “alias” when you register by choosing “Apply” at the upper right corner of the course description\, so that UCPH Accounting Department can make an internal transfer regarding payment of the Participation fee.\nIf you have been accepted for the course\, we will verify your enrollment via mail. \nPhD students enrolled at other universities than UCPH\n1) Choose “Apply” in the upper right corner of the course description.\nIf you have been accepted for the course\, we will send you a mail with a link to payment of the participant fee.\nNB: Payment via creditcard or Mobilepay only. After you have made your payment of the participation fee\, we will verify your enrollment via mail. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/methods-for-statistical-evaluation-of-ai-2/ CATEGORIES:DDSA-Funded Event,PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250811 DTEND;VALUE=DATE:20250816 DTSTAMP:20250310T104640Z CREATED:20240821T085338Z LAST-MODIFIED:20250310T104640Z UID:10001338-1754870400-1755302399@ddsa.dk SUMMARY:Quantitative Sustainability Assessment DESCRIPTION:Aim and content \nThe course is designed to equip students with the necessary tools to reflect upon and quantify sustainability performance\, a crucial component in research\, policymaking\, corporate decision making\, and reporting. It focuses on introducing the fundamental principles of quantitative sustainability assessment of various production systems\, including dynamic carbon modelling and utilizing unexplored side streams along value chains. \nKey methodological approaches such as material flow analysis\, input-output modelling\, life cycle assessment\, and carbon dynamics will be covered\, with an emphasis on defining functional units\, time scales\, and system boundaries. Participants will have the freedom to select from these methods to apply to their own data sets\, which they are encouraged to bring. This practical application will be facilitated through individual or group problem-oriented projects\, employing a variety of analytical tools and methods chosen by the students. \nThroughout the course\, critical topics such as resource and emission capture\, zero waste strategies\, and cascade utilization within circular business ecosystems will be explored through interactive lectures and practical applications. This approach fosters an environment of active learning\, allowing participants to directly engage with the material and apply what they learn to real-world scenarios. \nFormal requirements \nParticipants should have a Master’s degree or equivalent. Proficiency in data analysis with tools such as R\, Python\, MATLAB\, SAS\, or Excel is beneficial. A foundational understanding of life cycle thinking or knowledge of LCA is crucial. This background will enable effective engagement with the course’s focus on sustainable resource management and environmental evaluations. \nLearning outcome \nStudents who have completed the course will be able to: \nKnowledge:\n \n\nUnderstand the principles\, requirements\, and limitations behind quantitative sustainability assessment methods such as material flow analysis\, life cycle assessment (LCA)\, input-output modeling\, and dynamic carbon modeling.\nGrasp the importance of selecting meaningful parameters when defining system boundaries\, functional units\, and allocation methods.\nRecognize the role of unexplored sidestreams in enhancing sustainability within circular business ecosystems.\n\n\nSkills: \n\nApply various quantitative sustainability assessment tools and methods to their own data sets\, enhancing their ability to analyze and predict environmental impacts.\nSelect appropriate tools and methodologies freely\, including selected software or Excel\, to address specific research questions in process optimisation\, business or policy implications.\nBased on the aim of the assessment\, discuss\, identify and rank which factors should be included in a sustainability assessment.\nBe able to engage in the public debate about the sustainability of transitions.\n\n\nCompetences: \n\nDescribe and discuss quantitative sustainability assessment methods.\nBe able to reflect on how findings of a given sustainability assessment is influenced by the choice of critical parameters such as functional unit\, allocation methods\, and system boundaries.\nCritically assess and enhance the sustainability of different bioeconomic processes through evaluation techniques.\nEngage effectively in interdisciplinary settings\, utilizing their knowledge to contribute to sustainable development goals and circular economy initiatives.\nImplement resource management and systems design strategies to optimize sustainability and efficiency in various bioeconomic contexts.\n\nTarget group \nThis course is designed for PhD students engaged in various sectors of the bioeconomy\, particularly those from forestry\, food science\, agriculture\, biotechnology\, and environmental science. It is ideal for individuals interested in sustainable innovation in resource management and technologies within circular economy implementations across these critical areas. Emphasizing resource and emission capture and utilization\, zero waste practices\, and cascade utilization strategies\, the course prepares students to develop and implement circular business models that optimize resource use and minimize environmental impact. \nThe course aims to attract a diverse group of students\, including those in continuing education programs\, who are eager to apply circular economy principles to enhance sustainability in the forest and food sectors\, as well as other related bioeconomic fields. This interdisciplinary approach ensures that participants from different backgrounds can contribute to and benefit from the course\, fostering a comprehensive understanding of circular bioeconomy and promoting practices that lead to more sustainable and resilient bioeconomic sectors. \nTeaching and learning methods \nThe course is conducted through various teaching and learning methods: \nLectures: \nTo present the fundamentals and applications of quantitative sustainability methods\, such as dynamic carbon modeling and life cycle assessment\, within the context of circular economy principles. \nExercises: \nTo train students in applying these methods using a variety of tools\, enhancing their practical skills in sustainability analysis. \nCase Work: \nStudents are encouraged to bring their own data sets for problem-oriented projects\, applying the learned concepts to real-world scenarios either individually or in collaborative groups.\n \nDiscussions and Reflections: \nRegular sessions are held to discuss the opportunities and limitations of sustainability assessment\, fostering a critical understanding of resource management and systems design within various bioeconomic sectors. \nAssessment: \nCourse participation is assessed based on an individually prepared essay/report outlining the role of sustainability assessment in their own PhD project and how it could be assessed\, including pros and cons of the chosen method(s). \nLecturers \nCourse organisers: \n\nProfessor Lisbeth G. Thygesen\, Department for Geosciences and Natural Resource Management\nProfessor Marianne Thomsen\, Department of Food Science\nAssociate Professor Niclas Scott Bentsen\, Department for Geosciences and Natural Resource Management\n\nThe course will include guest lecture/-s from academia and/or industry\, will share practical insights into sustainability and circular bioeconomy\, enhancing the applicability of course concepts in real-world settings. \nRemarks \nNo course fee. The course will be cancelled if less than 10 students sign up for it.\n \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/quantitative-sustainability-assessment/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250630 DTEND;VALUE=DATE:20250705 DTSTAMP:20250310T104250Z CREATED:20250310T104250Z LAST-MODIFIED:20250310T104250Z UID:10001467-1751241600-1751673599@ddsa.dk SUMMARY:Introduction to Nutritional Metabolomics DESCRIPTION:Aim and content \nThe aim of this course is to introduce the students to all phases in a nutritional metabolomics study\, to instruct the students on sample handling\, and to train the student in data analysis and in the use of freely available tools for the metabolomics data flow. \nThe course will provide a general overview of LC-MS based untargeted metabolomics from study design to results and will be exemplified with its specific application in nutrition. It will be delivered using a mixture of lectures\, hands-on data preparation and analysis\, computer-based practical sessions\, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but there is no practical lab work. \nThe students will go through common steps in a typical metabolomics study using a real-life case. This case study includes plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF has been conducted and the students will further process and analyze the acquired data with various freeware tools (e.g. R\, XCMS\, MZmine etc). They will finally work on identification of relevant metabolites using manual analysis assisted by several web-based databases and structure elucidation tools. The course will be concluded by presentations of reports generated by the students based on the case study. \nThe students should expect a fairly technical course with a strong focus on the hands-on data analysis abilities and data interpretation skills. Programming skills are not a prerequisite for entering the course and students are guided through the exercises. However\, for students that are not familiar with R we expect them to explore the self-study curriculum based on short videos and texts that cover essential programming concepts.\nThe project work has a high workload\, and hence evening work can be expected during the course week. \nLearning outcome \nKnowledge:\n• Analyze different types of study designs commonly used in metabolomics (e.g.\, interventional study\, case-control\, cohort\, cross-sectional) and evaluate their strengths and weaknesses in terms of validity\, bias\, and applicability.\n• Explain each step of the metabolomics pipeline\, including sample collection\, data acquisition\, preprocessing\, statistical analysis\, and interpretation\, and identify appropriate tools and methods to perform each step effectively. \nSkills:\n• Carry out data preprocessing using freely available tools (R/XCMS)\n• Perform basic univariate and multivariate analysis (e.g. R)\n• Interpret the MS/MS spectra by manual interpretation and by utilizing available tools (e.g. MetFrag\, SIRIUS) and databases (e.g. HMDB\, METLIN and MassBank) \nCompetences:\n• Describe the handling of urine\, plasma and other samples collected from humans for metabolomics analysis\n• Understand the basic principles of UPLC-QTOF technology\n• Suggest which sample type to analyze for a specific research question and propose the relevant sample collection and preparation procedure \nTeaching and learning methods \nBasic principles and an overview are given by mostly frontal lectures that include small tasks and quizzes.\nEach practical step is then explained\, and the students work in groups to perform the tasks on the dataset provided.\nThe course culminates in a presentation where the students describe the steps they took and the conclusions they reached. \nLecturers \nAssistant Professor Jan Stanstrup\, NEXS\nAssociate Professor Henrik Munch Roager\, NEXS\nAssociate Professor Giorgia La Barbera\, NEXS\nAssociate Professor Carl Brunius\, Chalmers University of Technology\, Sweden\nPhD student Francesca Bucci\, NEXS \nRemarks \nBreakfast (light)\, snacks\, lunch and dinner will be provided during the course. \nFee\nThere is no fee for the PhD students under the Open Market in Denmark.\nOther participants are to pay a course fee of 700 EUR.\nYour registration is considered binding and the following rules apply:\n3 weeks before the start of the course\, it is possible to opt out\, without having to pay participation fees. If you opt out beyond this date or do not show up on the course you will be charged the full participation fee\, unless another participant are able to sign up for the course instead.\nThe fee must be paid no later than the 6th of June 2025.\nEach student must pay and arrange their own travel and accommodation in Copenhagen during the course. \nThe preliminary program can be downloaded here. \nFor more information please contact Jan Stanstrup – jst@nexs.ku.dk \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/introduction-to-nutritional-metabolomics/ LOCATION:Department of Nutrition Exercise and Sports CATEGORIES:PhD Course END:VEVENT END:VCALENDAR