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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: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: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:20261028
DTEND;VALUE=DATE:20261107
DTSTAMP:20260728T114659Z
CREATED:20260728T114659Z
LAST-MODIFIED:20260728T114659Z
UID:10001927-1793145600-1794009599@ddsa.dk
SUMMARY:Statistical methods for the SCIENCE
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 ContentIn this course\, students are assumed to be familiar with basic concepts of normal linear mixed effects models (e.g. as taught in the Statistics-variant of “Fundamentals of the PhD Education at SCIENCE – Module 2”). But to recap and extend the concepts of normal linear mixed effects models the course begins with an extension of the models with normally distributed responses (end-points) to similar models with binary\, ordinal and counting responses. After this we will focus on three statistical data analysis challenges that often appear in biology\, medicine\, and other empirical sciences; (1) statistical analysis of multivariate responses\, (2) statistical analysis of repeated measurements over time\, (3) in depth understanding of the multiple testing problem and associated choices needed in addressing this problem in relation to the underlying scientific questions. \nLearning outcomesIntended learning outcome for the students who complete the course:Knowledge:• Understand elements of frequentist statistics including estimation\, confidence intervals\, hypothesis tests\, model validation.• Understand data types and organization in tidy data.• Understand assumptions and limitations for statistical analyses.• Understand data structures for multivariate and repeated measurements data.• Understand the multiple testing problem and possible solutions. \nSkills:• Identify the structure and experimental design of a particular dataset.• Choose an adequate statistical model for multivariate data.• Choose an adequate statistical model for repeated measurements.• Choose an adequate method for correcting for multiple testing.• Use R via the RStudio interface to perform the statistical analysis. \nCompetences:• Formulate scientific questions in terms of statistical hypothesis.• Conduct statistical analysis using the discussed models.• Interpret the results of a statistical analysis.• Critically reflect over the results\, conclusions and limitations of a statistical analysis.• Judge when to seek help from a skilled statistician. \nTarget GroupAll PhD students who use statistical methods to analyze quantitative data. \nRecommended Academic QualificationsThis course builds upon the Statistics-variant of “Fundamentals of the PhD Education at SCIENCE – Module 2”. But everyone with an understanding of basic statistical models and methods\, and basic skills in using R may benefit from the course. \nResearch AreaAll SCIENCE research fields\, and secondarily other scientific fields with a statistical data analysis element (e.g. health sciences). \nTeaching and Learning MethodsThe teaching is done as a mixture of lectures and hands-on exercises on data examples. The exercises usually involve using R via the RStudio interface. \nType of AssessmentPass by attendance on minimum 4 of the 5 course days. \nLiteratureWill be announced later. \nCourse coordinatorProfessor Bo Markussen (bomar@math.ku.dk) \nDates2 + 3 days in two consecutive weeks:Wednesday\, October 28\, 2026Thursday\, October 29\, 2026Wednesday\, November 4\, 2026Thursday\, November 5\, 2026Friday\, November 6\, 2026 \nExpected frequencyAnnuallyThird and Second week before the beginning of block 2 \nCourse locationEither Frederiksberg or Nørre Campus. \nCourse fee• Participant fee: 0 DKK• 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: DK 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): 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/statistical-methods-for-the-science/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261109
DTEND;VALUE=DATE:20261114
DTSTAMP:20260728T114659Z
CREATED:20260728T114659Z
LAST-MODIFIED:20260728T114659Z
UID:10001699-1794182400-1794614399@ddsa.dk
SUMMARY:Fundamentals of the PhD education at SCIENCE  - module 2 - K12
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-k12/
CATEGORIES:PhD Course
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DTSTART;VALUE=DATE:20261116
DTEND;VALUE=DATE:20261121
DTSTAMP:20260728T114658Z
CREATED:20260728T114658Z
LAST-MODIFIED:20260728T114658Z
UID:10001957-1794787200-1795219199@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 ContentNumerical 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:1.	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:2.	Ability to use numerical optimization solutions in practice3.	Ability to use optimization toolboxes such as Python SciPy optimisation packages as well as others. \nCompetences:4.	Identify practical situations where numerical optimisation is needed.5.	Ability to formulate a problem as a numerical optimisation task.6.	Ability to choose a suitable optimization method \nTarget GroupPh.D. students in computer science\, mathematics\, chemistry\, economics and physics \nRecommended Academic QualificationsM.Sc in computer science\, mathematics\, chemistry\, economics and physics or equivalent. \nResearch AreaComputer Science\, mathematics\, chemistry\, economics and physics. \nTeaching and Learning Method5 full days with morning lecture and afternoon exercises \nType of AssessmentOne or two large take home assignments. \nLiteratureNumerical Optimization\, J. Nocedal and S. J. Wright. Springer. Course Notes. \nCourse coordinatorFrançois Lauze\, Associate Professor\, DIKU. \nGuest LecturerBernhard Kerbl\, Assistant Professor\, DIKU \nDates16th -20th November 2026.Enrolment deadline 9th October 2026. \nExpected frequencyOnce 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 locationNorth Campus or Frederiksberg Campus \nRegistrationRegistration with waiting list \nDeadline for registration4 weeks before course starts. If seats are available late registration might be accepted (with a cap on 30 participants) \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/numerical-optimization-2/
CATEGORIES:PhD Course
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DTSTART;VALUE=DATE:20261130
DTEND;VALUE=DATE:20261205
DTSTAMP:20260728T114658Z
CREATED:20260728T114658Z
LAST-MODIFIED:20260728T114658Z
UID:10001925-1795996800-1796428799@ddsa.dk
SUMMARY:Multivariate Data Analysis
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 Content \nAim:In industry and research huge amounts of physical\, chemical\, sensory and other quality measurements are produced on all sorts of materials\, processes and products. Exploratory data analysis / chemometrics offers a tool for extracting the optimal information from these data sets through the use of digitalization (modern software and computer technology).The course will give a step-by-step theoretical introduction to exploratory data analysis /chemometrics supported by practical examples from food science\, environmental science\, pharmaceutical science etc.Methods for exploratory analysis (Principal Component Analysis)\, multivariate calibration (Partial Least Squares) and basic data preprocessing are considered. The mathematics behind most of the concepts will be given together with the practical applications and considerations of the methods.Even more important\, though\, is the understanding and interpretation of the computed models.As is methods for outlier detection and model validation. Computer exercises and cases will be performed applying user-friendly software. A thorough introduction to the software will be given. \nCourse content:> Introduction to Multivariate Data Analysis> Principal Component Analysis (PCA)> Pre-processing> Outlier detection> Partial Least Squares Regression (PLSR)> Validation> Variable selection \nLearning outcomesKnowledge:• Describe chemometric methods for multivariate data analysis (exploration and regression)• Describe techniques for data pre-preprocessing• Describe techniques for outlier detection• Describe method validation principles• Understand the basics of the algorithms behind the PCA and PLS• Understand the math of data pre-processing \nSkills:• Apply theory on real life data analytical cases• Apply commercial software for data analysis• Interpret multivariate models (both exploratory and regression) \nCompetences:• Discuss and respond to univariate versus multivariate data analytical methodology in problem solving in society \nTarget GroupPhD students from any scientific field that gather data with several samples (+10) and manyvariables (+10) \nRecommended Academic QualificationsBasic statistical knowledge. \nResearch AreaAny that collect larger amounts of data\, i.e. environmental sciences\, chemistry\, food\, pharmaand biology \nTeaching and Learning MethodsThere will be a mixture of several different teaching methodologies:> Lectures (most also available as videos)> Exercises + Walkthrough> Short cases + Fish tank> Day cases + Debriefing sessions> Visual examples \nType of AssessmentA short report of three pages with data analysis on own/ provided data to be handed in and approved. This report may be written in groups of two. \nLiterature– Bro R\, Smilde AK (2014): Principal component analysis\, Analytical Methods\, 6\, 2812– Geladi P\, Kowalski BR (1986): Partial Least Squares Regression – A tutorial\, Analytical Chimica Acta\, 185\, 1-17– Rinnan Å\, van den Berg F\, Engelsen SB (2009): Review of the most common pre-processing techniques for near-infrared spectra\, Trends in Analytical Chemistry\, 28 (10)\, 1201-1222– Andersen CM\, Bro R (2010): Variable selection in regression – A tutorial\, Journal of Chemometrics\, 24\, 728-737– Kjeldahl K\, Bro R (2010): Some common misunderstandings in chemometrics\, Journal of Chemometrics\, 24\, 558-564– A series of short articles in Spectroscopy Europe named the Tony Davies Column \nCourse coordinatorÅsmund Rinnan \nExpected frequencyEvery year in the first week of December. \nCourse locationFrederiksberg Campus \nRequirements for signing upSeats to PhD students from other Danish universities will be allocated on a first-come\, first-served basis and according to the applicable rules. Applications from other participants will be considered after the deadline for registration. \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): 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/multivariate-data-analysis/
CATEGORIES:PhD Course
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