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DTSTART;VALUE=DATE:20260908
DTEND;VALUE=DATE:20261118
DTSTAMP:20260924T084251Z
CREATED:20260915T103006Z
LAST-MODIFIED:20260924T084251Z
UID:10001818-1788825600-1794959999@ddsa.dk
SUMMARY:Data Science Projects
DESCRIPTION:Enrolment guidelines This 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). 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 ContentData Science covers both Statistics and Machine Learning. This toolbox course provides a platform to perform research with the student’s own data using either Statistical data analysis\, Machine Learning methods\, or possibly a combination. A supervisor is assigned to each student for this work.The data may come from own experiments or observational data and be tables of numbers\, surveys in text or digital formats\, pictures\, scans\, or video. All related to some scientific investigation from the PhD student’s own work.Depending on the primary scope of each of data analysis problems\, an expert in either Statistics or Machine Learning will supervise the project. Typically\, Statistics projects use R\, and Machine Learning projects will use Python. However\, other software platforms are possible depending on the student’s qualifications.Typical analysis within the scope of Statistics is modelling of experimental data to establish an associative or causal relation between an outcome of interest and some explanatory variables\, e.g. application of different treatments. Subsequent\, to quantify relations that cannot be explained by biological variation but must be attributed to real effects.Typical analysis within the scope of Machine Learning could be automated quantification of objects of interests in the data (for example\, image analysis)\, combining different types of data to address a common research question (like combining text and measurements)\, or building predictive models using Machine Learning.The course report will be written like a draft of a research paper – that may ideally be completed and submitted following the course. One week prior to the course\, the participants must submit a synopsis with a short description of their data and the desired outcome. This will allow us to consider plenum lectures on some specific analysis methods and to plan the project supervisions. \nLearning outcomesIntended learning outcome for the students who complete the course: \nKnowledge•	Describe the analysis methods used by others for similar problems.•	Describe relevant\, alternative approaches for solving the problem. \nSkills•	Develop/adapt/extend a computer-based software method for quantification and/or analysis of their own data. \nCompetences•	Formulate scientific questions from their PhD project in terms of research hypotheses.•	Interpret the results of their computer-based analysis in relation to their PhD project. \nTarget GroupPhD students from all SCIENCE departments with a clear element of data science in their research project. \nRecommended Academic QualificationsThe students must either have some Statistics or Machine Learning experience\, corresponding to either the Statistical Methods for SCIENCE or Machine Learning for SCIENCE PhD toolbox courses.The students should have passed the PhD Fundamentals course Module 2. In addition\, the participants should have passed either the Statistical Methods for SCIENCE or Machine Learning for SCIENCE PhD toolbox courses\, or have similar experience. If in doubt\, contact the course responsible \nResearch AreaAll SCIENCE research fields\, and secondarily other scientific fields with a data science element (e.g. health sciences). \nTeaching and Learning MethodsThe first few course days may include traditional lectures. Following this\, most of the work will be organized during individual supervision meetings with the course lecturers. The project must result in a research article style report and be presented before the class at the concluding seminar. The projects may be done using software packages of the participant’s own choice. \nType of AssessmentThe students need to hand in their reports at the end of the course. The report must be approved. The students are allowed to work in 2-person groups. \nLiteratureThis depends on the individual project.For potential background literature\, see the course pages for the Python for SCIENCE\, Statistical methods for SCIENCE\, or Machine Learning for SCIENCE; all listed on the Data Science Lab homepage. \nCourse coordinatorErik Dam – erikdam@di.ku.dk \nDates08 September – 17 November 2026. Course last for 2-3 months where the meetings are coordinated between student and supervisor. \nCourse locationPhysically on campus for initial plenum session(s).Most of the course is coordinated between the PhD student and the assigned project supervisor. \n \nCourse fee• 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: 4800 DKK• PhD student from foreign university: 4800 DKK• Master’s student from Danish university: 0 DKK• Master’s student from foreign university: 4800 DKK• Non-PhD student employed at a university (e.g.\, postdocs): 4800 DKK • Non-PhD student not employed at a university (e.g.\, from a private company): 12.000 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.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/data-science-projects/
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:20260924T084249Z
CREATED:20260728T114659Z
LAST-MODIFIED:20260924T084249Z
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
END:VEVENT
BEGIN:VEVENT
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
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20261116
DTEND;VALUE=DATE:20261121
DTSTAMP:20260909T074411Z
CREATED:20260909T074411Z
LAST-MODIFIED:20260909T074411Z
UID:10001946-1794787200-1795219199@ddsa.dk
SUMMARY:Methods in forest microbial ecology and eDNA – from sampling to data analysis
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 course is designed to provide an understanding of microbiome analysis\, from sample collection to advanced data interpretation\, fostering the development of skills necessary for addressing ecological questions related to microbial communities.\nThe course offers a comprehensive exploration of microbiome data analysis with a specific focus on its application in the field of forest ecology. \nThe course is structured around the following components:\n• Lectures on Metabarcoding in Ecological Applications: Principles\, methodologies\, and applications of metabarcoding techniques within the context of forest ecology. Lectures will cover the latest advancements in DNA sequencing technologies and bioinformatics tools essential for understanding microbial communities in forest ecosystems. \n• Hands-on eDNA Sampling: Practical sessions where participants gain firsthand experience in collecting environmental DNA (eDNA) samples from forest ecosystems\, including sampling techniques\, preservation methods\, and quality assurance considerations. \n• Molecular Lab Analysis: Guidance through molecular laboratory analysis of eDNA samples\, covering DNA extraction\, PCR amplification\, and sequencing techniques. \n• Principles of Bioinformatics: Foundational introduction to bioinformatics tools and workflows for processing and interpreting large-scale microbiome datasets\, including data quality control and taxonomic assignment. \n• Practical Exercises on Microbiome Data Analysis using R: Exercises on data preprocessing\, statistical analysis\, and visualization of microbiome datasets derived from forest environments. Emphasis is placed\non ecological interpretation and integration of microbial data into ecosystem-level questions. \n• Written Report: The course concludes with an individual or group research project where participants analyze microbiome data (either provided or from their own research) and prepare a written report\ndetailing findings and ecological interpretations. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\n• Gain in-depth knowledge of the principles\, methodologies\, and applications of metabarcoding in ecological research.\n• Demonstrate a thorough understanding of eDNA sampling techniques\, including sample collection\, preservation\, and quality control.\n• Understand bioinformatics tools specific to microbiome datasets and their application to ecological interpretation. \nSkills:\n• Develop hands-on skills in implementing metabarcoding workflows\, from experimental design to data analysis.\n• Demonstrate competence in troubleshooting challenges in molecular and bioinformatic workflows.\n• Apply R programming skills to perform advanced analysis of microbiome datasets (diversity metrics\, differential abundance\, and community structure).\n• Conduct eDNA sampling with methodological accuracy and reproducibility. \nCompetences:\n• Design and execute research projects involving microbiome analysis in forest ecology.\n• Integrate metabarcoding techniques\, eDNA sampling\, and data analysis to address ecological questions.\n• Demonstrate autonomy and critical reflection when interpreting complex microbiome data for ecological applications. \nTarget Group\nPhD students working with ecology and related ecological disciplines involving microbiome data. The course is relevant for students applying molecular and bioinformatic tools to understand biodiversity and ecosystem functioning. \nRecommended Academic Qualifications\nExperience working with the R environment; knowledge of community ecology metrics and analysis; basic experience in molecular lab techniques. \nResearch Area\nEcology\, Microbiology\, Environmental Science. \nTeaching and Learning Methods\n1. Lectures introducing microbiome analysis workflows and ecological applications.\n2. Hands-on collection and processing of eDNA samples.\n3. Practical exercises on microbiome data analysis using R (including work with participants’ own data). \nType of Assessment\nCompletion of exercises and submission of a written report based on microbiome data analysis. \nLiterature\nSelected scientific papers and tutorial materials will be provided before the course. \nCourse coordinator\nSebastian Kepfer Rojas\, Associate Professor\, Department of Geoscience and Natural Resource Management\nEmail: skro@ign.ku.dk\, Phone: +45 353 34843 \nGuest Lecturers\nProf. Leho Tedersoo\, Mycology and Microbiology Center\, University of Tartu (EE) — expert in microbiome and molecular identification methods; lectures on next-generation sequencing and metabarcoding applications in forest ecosystems. \nDr. Sofia Fernandes Gomes\, Institute of Biology\, University of Leiden (NL) — expert in above- belowground interactions and microbiome sequence analysis using R; leads data analysis exercises and theory sessions. \nDr. Giovanni Emiliani\, Institute of Sustainable Plant Protection\, NRC (IT) — expert in molecular and functional characterization of fungal species and endophytic bacteria; lectures on functional ecology and plant–microbe interactions in forest ecosystems. \nDates\n16 – 20 November 2026 \nExpected frequency\nTo be held biennially. \nCourse location\nUniversity of Copenhagen\, Department of Geoscience and Natural Resource Management. \nRequirements for signing up\nOpen to PhD students; basic experience in R and ecology required. Registration with waiting list. \n• Participant fee: DKK 1.000\n• PhD student enrolled at SCIENCE: DKK 0\n• PhD student from Danish PhD school Open market: DKK 0\n• PhD student from Danish PhD school not Open market: DKK 3.000\n• PhD student from foreign university: DKK 3.000\n• Master’s student from Danish university: DKK 0\n• Master’s student from foreign university: DKK 3.000\n• Non-PhD student employed at a university (e.g.\, postdocs): DKK 3.000\n• Non-PhD student not employed at a university (e.g.\, from a private company): DKK 8.400 \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/methods-in-forest-microbial-ecology-and-edna-from-sampling-to-data-analysis/
LOCATION:Department of Geoscience and Natural Resource Management
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
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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BEGIN:VEVENT
DTSTART;VALUE=DATE:20270120
DTEND;VALUE=DATE:20270130
DTSTAMP:20260909T074604Z
CREATED:20260909T074604Z
LAST-MODIFIED:20260909T074604Z
UID:10001975-1800403200-1801267199@ddsa.dk
SUMMARY:Python for 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).\nSeats 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 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 course 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\nresults (code architecture\, versioning\, open source). The course days will interchange between lectures and hands-on programming exercises. \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 departments with an element of data science in their research project. \nRecommended Academic Qualifications\nNone. \nResearch Area\nThe examples in the exercises come primarily from SCIENCE research fields and secondarily from health sciences. But the course may be relevant for all scientific fields with a clear data science element. \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\, DIKU. \nGuest Lecturers\nNone. \nDates\nWed 20\, Fri 22\, Mon 25\, Wed 27\, Fri 29\, all January 2027\, from 9 to 16 all days. \nExpected frequency\nYearly at approximately the same dates. \nCourse location\nUniversity of Copenhagen\, Nørre Campus \nCourse fee\n• Participant fee: DKK 0\n• PhD student enrolled at SCIENCE: DKK 0\n• PhD student from Danish PhD school Open market: DKK 0\n• PhD student from Danish PhD school not Open market: DKK 3.000\n• PhD student from foreign university: DKK 3.000\n• Master’s student from Danish university: DKK 0\n• Master’s student from foreign university: DKK 3.000\n• Non-PhD student employed at a university (e.g.\, postdocs): DKK 3.000\n• Non-PhD student not employed at a university (e.g.\, from a private company): DKK 8.400 \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/python-for-science-3/
CATEGORIES:PhD Course
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20270427
DTEND;VALUE=DATE:20270526
DTSTAMP:20260909T074306Z
CREATED:20260909T074306Z
LAST-MODIFIED:20260909T074306Z
UID:10001947-1808784000-1811289599@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 MachineLearning. 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\,\nmachine 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.\nThis 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 inter-disciplinary data science methods in their research. \nTarget Group\nPhD students from all SCIENCE departments with a clear element of data science in their research project. PhD students from other departments with similar data science approaches are also welcome (e.g. SUND). \nRecommended Academic Qualifications\nWe use Python for the examples and exercises\, so a basic level of Python programming experience is needed. \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. We will use data\, examples\, and other material from publicly available sources. \nCourse coordinator\nErik Dam\, DIKU. \nDates\nThe course runs across five consecutive Tuesdays: Apr 27\, May 4\, May 11\, May 18\, May 25\, all 2027. \nExpected frequency\nThe course is repeated annually at the approximately same dates. \nCourse location\nUniversity of Copenhagen\, Nørre Campus \nRequirements for signing up\nThe academic requirements listed above.\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.\nPost Docs and assis/assoc/full Professors and Masters students from SCIENCE are welcome if there are free seats. \nCourse fee\n• PhD student enrolled at SCIENCE: DKK 0\n• PhD student from Danish PhD school Open market: DKK 0\n• PhD student from Danish PhD school not Open market: DKK 3.000\n• PhD student from foreign university: DKK 3.000\n• Master’s student from Danish university: DKK 0\n• Master’s student from foreign university: DKK 3.000\n• Non-PhD student employed at a university (e.g.\, postdocs): DKK 3.000\n• Non-PhD student not employed at a university (e.g.\, from a private company): DKK 8.400 \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-3/
CATEGORIES:PhD Course
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