BEGIN:VCALENDAR VERSION:2.0 PRODID:-//DDSA - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:DDSA X-ORIGINAL-URL:https://ddsa.dk X-WR-CALDESC:Events for DDSA REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Copenhagen BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20271031T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20260420 DTEND;VALUE=DATE:20260430 DTSTAMP:20260428T033321 CREATED:20260119T135436Z LAST-MODIFIED:20260309T135309Z UID:10001784-1776643200-1777507199@ddsa.dk SUMMARY:Machine Learning (2026) DESCRIPTION:Welcome to Machine Learning (2026) \nDescription: Machine learning revolves around creating computer programs that enable machines to learn from examples or experiences. It’s an interdisciplinary field at the intersection of computer science\, engineering\, statistics\, and pattern recognition. In recent decades\, it has witnessed rapid theoretical progress and extensive real-world applications across various domains. These applications encompass machine perception (like speech recognition and computer vision)\, natural language processing (including large language models)\, time-series prediction\, sciences\, recommendation systems\, medical diagnosis and prognosis\, autonomous vehicles\, predictive maintenance\, sentiment analysis\, and beyond. Machine learning serves as a driving force behind the ongoing wave of artificial intelligence. \nThis course offers a comprehensive introduction to machine learning\, with the goal of elucidating fundamental methods and their theoretical underpinnings\, while also addressing practical machine learning problems such as pattern recognition\, prediction\, clustering\, and generative modeling. \nTopics will include: \n– Supervised learning methods: logistic regression\, support vector machines\, neural networks\, K-nearest neighbors\, and decision trees \n– Unsupervised learning and clustering methods: K-means\, Gaussian mixture models\, Expectation Maximization algorithm\, and principal component analysis \n– Deep learning methods: deep neural networks\, long short-term memory recurrent neural networks\, convolutional neural networks\, generative adversarial networks\, and Transformers. \n– Probabilistic graphical models  – Reinforcement learning \nFor additional information\, updates\, and registration\, please refer to AAU PhD Moodle via the link provided on the right side of this page. \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-2026/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260420T090000 DTEND;TZID=Europe/Copenhagen:20260428T150000 DTSTAMP:20260428T033321 CREATED:20250602T114106Z LAST-MODIFIED:20250602T114106Z UID:10001644-1776675600-1777388400@ddsa.dk SUMMARY:Bioinformatics DESCRIPTION:Learn about bioinformatics analysis of genomic\, transcriptomic\, and proteomic data. \nDuring this course\, you will gain knowledge of the most essential databases and methods for molecular sequence and structure analysis. \nThese years\, computer based methods play a crucial role in molecular biology\, microbiology\, and personalised medicine. Huge international databases of sequence and structure contain information\, which in many cases can entirely replace experimental work\, and in other cases can be used to optimize the benefit of experimental resources. \nIntroduction to Bioinformatics is a practically oriented course with focus on using the methods rather than deriving them mathematically. Bioinformatics is presented as a biological discipline rooted in evolutionary theory. A large part of the course consists of computer-based exercises\, where the computational tools are applied based on the participants’ biological prior knowledge. \nThe course is part of the Master programme in Personalised Medicine offered by The University of Copenhagen. URL:https://ddsa.dk/event/bioinformatics/ LOCATION:DTU\, Building 303A aud. 42\, DTU Lyngby Campus\, Lyngby\, Denmark CATEGORIES:Other Events,PhD Course ATTACH;FMTTYPE=image/jpeg:https://ddsa.dk/wp-content/uploads/2025/05/Bioinformatics_1100x600.jpg END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260422 DTEND;VALUE=DATE:20260505 DTSTAMP:20260428T033321 CREATED:20260309T140937Z LAST-MODIFIED:20260309T140937Z UID:10001906-1776816000-1777939199@ddsa.dk SUMMARY:Analysis of genome-wide enrichment data DESCRIPTION:Enrolment guidelines \nThis course is free of charge for PhD students at Danish universities (except Copenhagen Business School)\, and for PhD Students from NorDoc member faculties. All other participants must pay the course fee. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university\, you will be placed on the waiting list until enrollment deadline. This also applies to PhD students from NorDoc member faculties. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nLearning objectives\nA student who has met the objectives of the course will be able to: \n1. Process enrichment-based sequencing data (e.g.\, ChIP-seq\, CUT&RUN/CUT&Tag\, ATAC-seq\, DRIP-seq\, Repli-seq) into outputs you can visualize and analyze. \n2. Evaluate and interpret dataset quality by applying appropriate QC metrics and justify whether the data are suitable for downstream analysis. \n3. Retrieve\, process\, and integrate public genome-wide datasets and reproduce key published results and figures. \n4. Critically assess how strong published conclusions are by evaluating experimental design\, analysis choices\, and whether data actually support the claims. \n5. Design and justify an new end-to-end analysis workflow (for own data or a chosen paper)\, and recommend improvements that increase robustness (controls\, replication\, QC criteria\, alternative analyses). \nContent\nIn functional genomics\, a growing set of methods based on high-throughput sequencing of enriched DNA or RNA fractions has enabled genome-wide analyses of regulatory and chromatin-associated processes. These approaches have shifted the focus from single loci to system-level interpretation across the genome. Methods such as ChIP-seq\, ChIP-exo\, CUT&RUN\, CUT&Tag\, ATAC-seq\, Repli-seq\, MiDAS-seq\, DRIP-seq\, Tracking-seq\, DISCOVER-seq\, and related assays are widely used to study chromatin accessibility\, transcriptional regulation\, DNA replication dynamics\, replication stress\, and DNA repair in development and disease as well as identification of CRISPR and base-editing targets. \nThis course enables participants to process\, analyse\, and interpret data from enrichment-based sequencing assays. The emphasis is on stepwise development of practical skills through hands-on exercises. By the end of the course\, participants will be able to run reproducible analysis workflows and reproduce selected published analyses/figures using their own and deposited datasets. \nIn addition\, the course provides the theoretical foundation needed to interpret results responsibly\, including data structures and formats\, core analysis workflows\, visualisation strategies\, and key limitations and biases. Real-life case studies from the literature will illustrate how these assays and analyses support (or fail to support) scientific conclusions. \nParticipants\nThe course is intended for PhD students with a interest in cell biology\, genetics\, genomics or disease biology who plan to use enrichment-based high-throughput sequencing assays (e.g.\, ChIP-seq\, CUT&RUN/CUT&Tag\, ATAC-seq\, DRIP-seq\, Repli-seq\, meRIP-seq) and want to be able to analyse and interpret the resulting data. \nThe course uses tools with graphical user interfaces and does not require prior experience with scripting or command-line workflows. Participants are expected to be comfortable with basic biological concepts (DNA/RNA\, chromatin\, gene regulation) and routine file handling. Participants are encouraged to indicate whether they have project data and the data type when signing up\, and where feasible course exercises may be adapted to reflect participant interests. \nRelevance to graduate programmes\nThe course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences\, UCPH: \nCellular and Genetic Medicine \nMolecular Mechanisms of Disease \nBiostatistics and Bioinformatics \nLanguage\nEnglish \nForm\nHands-on exercises\, lectures\, group work & presentations\, and assignments (14 hours). \nCourse director\nMads Lerdrup\, Associate Professor\, Center for Chromosomal Stability\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\, mlerdrup@sund.ku.dk\nDaniel Messerschmidt\, Associate Professor\, Center for Chromosomal Stability\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\, mlerdrup@sund.ku.dk \nTeachers\nMads Lerdrup\, Associate Professor\, Center for Chromosomal Stability\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\nDaniel Messerscmidt\, Associate Professor\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\nMika Zagrobelny\, Associate Professor\, Center for Chromosomal Stability\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\nErkut Ilaslan\, Post doc\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\nMirra Søegaard\, Research Assistant\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\nAli Altintas\, Assistant Professor\, NNF Center for Basic Metabolic Research\, Faculty of Health Sciences\, University of Copenhagen\nKathleen Stewart-Morgan\, Associate Professor\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\nHeike Wollmann\, Special Consultant\, ReNEW\, Faculty of Health Sciences\, University of Copenhagen\nSmaragda Kompocholi\, Post doc\, CGEN\, Department of Molecular Medicine\, Faculty of Health Sciences\, University of Copenhagen\nJohn Arne Dahl\, Group leader\, Oslo University Hospital\nAditya Sankar\, Scientific Visitor Programme Lead\, EMBL Heidelberg\nPaul Cloos\, Biohagen Aps \nDates\n22 April – 4 May 2026 \nDuration\n22 – 28 April: 9-16 \n29 April: 9-14 \n30 April – 4 May: Assignment work with 2-3 hours of daily supervision. \nCourse location\nThe Panum Building\, University of Copenhagen \nExpected frequency\nOnce a year in the spring \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 last day of enrolment. \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student\, your participation in the course must be in agreement with your principal supervisor. \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/analysis-of-genome-wide-enrichment-data/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260422 DTEND;VALUE=DATE:20260523 DTSTAMP:20260428T033321 CREATED:20260309T141034Z LAST-MODIFIED:20260309T141034Z UID:10001945-1776816000-1779494399@ddsa.dk SUMMARY:Intuition and Interpretation in Causal Inference DESCRIPTION:Department of Political Science \nDates and time: 22-23 April and 21-22 May 2026 from 9:00 to 16:00 \nMany graduate courses in causal inference equip students with powerful tools for drawing causal conclusions from observational data. These courses often emphasize either the formal\, mathematical conditions under which causal identification is possible\, or the practical implementation of methods in statistical software. While both approaches are essential\, they can leave students with limited intuition for why particular designs work\, what violations of identifying assumptions look like in practice\, and how to meaningfully interpret the resulting estimates. \nThis PhD course focuses on building intuition for common causal inference designs. Rather than centering on proofs or step-by-step estimation procedures\, the course emphasizes substantive understanding of identification strategies\, realistic threats to identification\, and careful interpretation of causal estimates. Particular attention is paid to the meaning of causal estimands – what exactly is being estimated\, whom it applies to\, and under what assumptions – and how these estimands relate to substantive research questions. And how does the estimand change under different specifications of the same general method? \nThe course assumes basic familiarity with causal inference concepts and designs. We will briefly revisit randomized controlled trials (RCTs) as a conceptual benchmark\, before covering instrumental variables\, regression discontinuity designs\, and difference-in-differences. These designs are not covered as abstract techniques but are discussed and applied as research strategies embedded in substantive empirical contexts. \nThrough this approach\, the course provides practical intuition for several important recent developments in applied econometrics. Topics include sensitivity analysis for unobserved confounding\, staggered difference-in-differences designs under heterogeneous treatment effects\, violation of parallel trends\, profiling and interpretation of compliers in instrumental-variables settings\, and power analysis for observational studies. \nThe course runs over four days in total. The first two days take place in April\, and the final two days in May. Participants will have the option to write an essay on causal identification in their own dissertation project for additional ECTS credit. \nFormat\nThe course runs April 23-24\, 2026\, then there will be a one-month break\, allowing students to work on their essays. Class meets again on May 21-22\, 2026. The teaching format is highly interactive. Each causal inference design is introduced through an interactive lecture organized around a central case paper which is discussed throughout the session to illustrate identification logic\, assumptions\, and interpretation. This is followed by group discussion of an applied paper\, where participants critically assess threats to identification\, interpret the reported estimates\, and discuss ways the analysis could be strengthened. \nOptional essay\nParticipants will have the option to write a five-page essay about the topics discussed throughout the course\, and how they relate to their own work. Completing this essay will result in additional ECTS points (3 points instead of 2). The course organizers will provide feedback and help think through the causal identification questions raised in all written essays. The deadline for submitting papers is Thursday 14 May 2026\, at noon. \nCourse organisers and lecturers:\nFlorian Hollenbach\, Associate Professor\, Copenhagen Business School\nBenjamin Egerod\, Assistant Professor\, Copenhagen Business School \nPreliminary program: Keywords and readings \nDay 1: \n\nRCT (recap)\n\nCausality and identification\nAverage treatment effects\, heterogeneity and causal estimands\nReadings:\nChapter 4 in Cunningham (2021)\nCase text: Blattman and Dercon (2018)\n\n\nInstrumental variables\n\nIdentifying assumptions: Conditional independence\, excludability\, SUTVA.\nCompliers\, always-takers\, never-takers.\nCausal estimand: Local average treatment effect (LATE)\nSensitivity analysis\nReadings:\nChapter 7 in Cunningham (2021)\nBetz\, Cook and Hollenbach (2019)\nMarbech and Hangartner (2020)\nCase text 1: Clingingsmith et al (2009)\nCase text 2: Baron and Gross (2025)\nListen to this episode of the podcast series Probable Causation: Episode 64: Jason Baron on Foster Care Placement. Link: https://www.probablecausation.com/podcasts/episode-64-jason-baron. There is an extremely informative substantive discussion of compliance and how it matters for interpretation in the estimates (what we call the estimand).\n\n\n\nDay 2: \n\nRegression Discontinuity\n\nIdentification: Smoothness of potential outcomes vs. smoothness of compliers\nThe LATE and external validity\nEffective sample size and power\nRobustness to specification choice\nReadings:\nChapter 6 in Cunningham (2021)\nMarshall (2024)\nCase text 1: Eggers and Hainmueller (2009)\nCase text 2: Dell and Querubin (2018)\n\n\n\nDay 3: \n\nDifference-in-differences\n\nCanonical design (brief recap): identification and causal estimand\nStaggered uptake and heterogeneity\nMore on identification: What does the parallel trends assumption mean?\nSensitivity analysis for violation of parallel trends\nReadings:\nChapter 9 in Cunningham (2021)\nEgerod and Hollenbach (2024)\nRambachan and Roth (2023)\nGhanem\, Sant’Anna and Wüttrich (2022)\nCase text 1: Egerod and Fouirnaies (2023)\nCase text 2: Dinas et al (2019)\n\n\n\nDay 4: \n\nStatistical power in observational studies\n\nBias and variance\nError types\nReadings:\nEgerod and Hollenbach (2024)\n\n\n\nLanguage: English \nECTS: 2 points for attendance including preparation + 1 extra point for participation with essay (optional). \nMax. numbers of participants: 12 \nCourse fee: The PhD School at the Faculty of Social Sciences participates in Denmark’s national network for PhD courses. This course is free of charge for PhD students enrolled at one of the participating PhD schools (PhD students enrolled at a Danish University\, except Copenhagen Business School). Other PhD students will be charged a course fee of DKK 1\,200 per ECTS for participation in the course (PhD students enrolled at Copenhagen Business School or a University outside Denmark). \nRegistration: Please register via the link in the box no later than 24 March 2026. \nFurther information: For more information about the PhD course\, please contact the PhD Administration (phd@hrsc.ku.dk). \nLiterature \nBaron\, E. Jason\, and Max Gross. “Is there a foster care-to-prison pipeline? Evidence from quasi-randomly assigned investigators.” Review of Economics and Statistics (2025): 1-46. \nBlattman\, Christopher\, and Stefan Dercon. “The impacts of industrial and entrepreneurial work on income and health: Experimental evidence from Ethiopia.” American Economic Journal: Applied Economics 10\, no. 3 (2018): 1-38. \nBetz\, Timm\, Scott J. Cook\, and Florian M. Hollenbach. “Spatial interdependence and instrumental variable models.” Political science research and methods 8\, no. 4 (2020): 646-661. \nClingingsmith\, David\, Asim Ijaz Khwaja\, and Michael Kremer. “Estimating the impact of the Hajj: religion and tolerance in Islam’s global gathering.” The Quarterly Journal of Economics 124\, no. 3 (2009): 1133-1170. \nCunningham\, Scott. Causal inference: The mixtape. Yale university press\, 2021. \nDell\, Melissa\, and Pablo Querubin. “Nation building through foreign intervention: Evidence from discontinuities in military strategies.” The Quarterly Journal of Economics 133\, no. 2 (2018): 701-764. \nDinas\, E.\, Matakos\, K.\, Xefteris\, D.\, & Hangartner\, D. (2019). Waking up the golden dawn: does exposure to the refugee crisis increase support for extreme-right parties? Political Analysis\, 27(2)\, 244-254. \nEgerod\, Benjamin & Alexander Fouirnaies (2024): How Does Electoral Accountability Affect Legislator Behavior? Evidence from Life-Tenured Legislators in Denmark. Unpublished Manuscript. \nEgerod\, Benjamin CK\, and Florian M. Hollenbach. “How many is enough? Sample size in staggered difference-in-differences designs.” OSF Preprint (2024). \nEggers\, Andrew C.\, and Jens Hainmueller. “MPs for sale? Returns to office in postwar British politics.” American Political Science Review 103\, no. 4 (2009): 513-533. \nGhanem\, Dalia\, Pedro HC Sant’Anna\, and Kaspar Wüthrich. “Selection and parallel trends.” arXiv preprint arXiv:2203.09001 (2022). \nMarbach\, Moritz\, and Dominik Hangartner. “Profiling compliers and noncompliers for instrumental-variable analysis.” Political Analysis 28\, no. 3 (2020): 435-444. \nMarshall\, John. “Can close election regression discontinuity designs identify effects of winning politician characteristics?.” American Journal of Political Science 68\, no. 2 (2024): 494-510. \nRambachan\, Ashesh\, and Jonathan Roth. “A more credible approach to parallel trends.” Review of Economic Studies 90\, no. 5 (2023): 2555-2591. \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/intuition-and-interpretation-in-causal-inference/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260423 DTEND;VALUE=DATE:20260501 DTSTAMP:20260428T033321 CREATED:20260204T133730Z LAST-MODIFIED:20260204T133730Z UID:10001792-1776902400-1777593599@ddsa.dk SUMMARY:Artificial intelligence for scientific writing DESCRIPTION:This course aims to impart knowledge about and give participants an introduction to and practical experience using artificial intelligence (AI) tools to enhance their scientific writing processes.Disclaimer:\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/artificial-intelligence-for-scientific-writing/ LOCATION:Aarhus CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260427 DTEND;VALUE=DATE:20260502 DTSTAMP:20260428T033321 CREATED:20260204T133420Z LAST-MODIFIED:20260309T141327Z UID:10001849-1777248000-1777679999@ddsa.dk SUMMARY:Mixed Models with Biomedical and Engineering Applications Blocks DESCRIPTION:Description: Mixed models provide a flexible framework for analyzing data with multiple sources of random variation and they are indispensable in many medical\, biological\, and engineering applications. When treatments are tested in medical applications\, the responses for individuals receiving the same treatment often vary due to unobserved genetic factors and this variation must be taken into account when comparing different treatments. Similarly\, in agricultural field trials\, random soil variation affects the yield within plots. In quality control applications\, the variability of the output of a production process may\, apart from random noise\, e.g. depend on the batches of raw material used and the employee involved in the manufacturing process. \nThe course will provide an introduction to statistical analysis with linear mixed models. Linear mixed models is a unified framework for classical random effects ANOVA models\, random coefficient models and linear models for longitudinal data with associated user-friendly implementations in R and SPSS. Linear mixed models moreover provide generalizations of the classical models to complex data not covered by the standard statistical toolbox. \nThe course will focus on modeling with mixed models\, on how a statistical analysis can be carried out for a mixed model\, and on interpretation of models and results. Hands-on experience with real data will be obtained through computer exercises. \nPrerequisites: A basic knowledge of statistics (linear regression) and probability theory (random variables\, expectation\, variance and covariance) is expected. \nFor additional information\, updates\, and registration\, please refer to AAU PhD Moodle via the link provided on the right side of this page. \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/mixed-models-with-biomedical-and-engineering-applications-blocks/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260428 DTEND;VALUE=DATE:20260527 DTSTAMP:20260428T033321 CREATED:20260204T133452Z LAST-MODIFIED:20260309T141351Z UID:10001734-1777334400-1779839999@ddsa.dk SUMMARY:Machine Learning for SCIENCE (MLS) DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe Machine Learning for SCIENCE (MLS) course introduces key analysis methods in Machine Learning. These methods allow investigations of scientific data from most fields\, including data from physical measurements\, questionnaires\, pictures\, internet searches\, satellites\, and biochemical analyses. We cover data cleaning (e.g. missing data\, denoising)\, feature extraction\, machine learning basics (labels\, variables\, parameter optimization\, overfitting\, cross-validation)\, key machine learning and image analysis methods based on both unsupervised and supervised learning\, and visualization. Method-wise\, we start at Linear Discriminant Analysis and end with Deep Learning.\nAt the end of the course\, the students must write a report with a suggestion for an analysis ideally performed on their own research data including a small implementation of a key concept. This report could form the basis for the Data Science Projects PhD course also offered by the Data Science Lab. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge\n• Understand key machine learning concepts (e.g. parameter training\, overfitting).\n• Understand key machine learning methods (e.g. LDA\, supervised learning).\n• Understand key data analysis methods (e.g. feature extraction). \nSkills\n• Develop/adapt/extend a computer-based software method for analysis of relevant data. \nCompetences\n• Propose relevant analysis methods for scientific data science problems.\n• Consider cross-disciplinary data science methods in their research. \nTarget Group\nPhD students from all SCIENCE departments with an element of data science in their research project. \nRecommended Academic Qualifications\nWe use Python for the examples and exercises\, so a basic level of Python programming experience is needed.\nThe Python skills could come from the Python for SCIENCE PhD toolbox course. \nResearch Area\nAll SCIENCE research fields\, and secondarily other scientific fields with a data science element (e.g. health sciences). \nTeaching and Learning Methods\nThe course is composed of sessions combining lectures and exercises.\nFor each topic\, the students will get hands-on experience in applying\, modifying\, and programming analysis methods.\nThe programming examples will be implemented using Python in JupyterLab notebooks. \nType of Assessment\nThe students need to hand in their reports (10 days after the final course day) that must be approved. The students are allowed to work in 2-person groups. \nLiterature\nCourse lecture slides and exercises.\nWe will use data\, examples\, and other material from publicly available sources. \nCourse coordinator\nErik Dam\, Professor\, erikdam@di.ku.dk \nDates\n2026: Apr 28\, May 5\, May 12\, May 19\, May 26. \nCourse location\nPhysically on campus.\nTypically\, at Nørre Campus\, alternatively at Frederiksberg Campus. \nCourse fee\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3600 DKK\n• PhD student from foreign university: 3600 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3600 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3600 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 10.080 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/machine-learning-for-science-mls-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260501T090000 DTEND;TZID=Europe/Copenhagen:20260619T150000 DTSTAMP:20260428T033321 CREATED:20250602T114136Z LAST-MODIFIED:20250602T114136Z UID:10001645-1777626000-1781881200@ddsa.dk SUMMARY:Data Science DESCRIPTION:Learn to analyse large data sets and train machine learning methods. \nIncreasing amounts of data are being collected in the healthcare system from high throughput genomics\, wearable devices\, and electronic patient records. This course will provide you with the necessary data science skills required to analyse such large datasets. \nWe will cover the various data analysis steps from loading and transforming data to visualization\, statistical analysis\, and machine learning (both supervised and unsupervised learning). \nYou will learn about tools that can help make clear and reproducible analyses such as software for version control and workflow management and be introduced to the use of High-Performance Computing (HPC) and parallelization. \nThe course will be hands-on where you will analyse relevant data sets combined with a systematic review of the various methods and tools\, including sources of error\, variation\, and uncertainty. \nThe data analysis will be done using R (tidyverse) and experience with the use of R is an advantage. Experience with R can possibly be gained by self-study in connection with the course. URL:https://ddsa.dk/event/data-science/ LOCATION:Aarhus University\, Aarhus C CATEGORIES:Other Events,PhD Course ATTACH;FMTTYPE=image/jpeg:https://ddsa.dk/wp-content/uploads/2025/05/Datascience_1100x600px.jpg END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260504 DTEND;VALUE=DATE:20260509 DTSTAMP:20260428T033321 CREATED:20260205T121650Z LAST-MODIFIED:20260309T141452Z UID:10001717-1777852800-1778284799@ddsa.dk SUMMARY:Estimating Causal Effects with Observational Data DESCRIPTION:Enrolment guidelines \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nResearchers are often interested in investigating causal relationships\, i.e.\, if and how one variable affects another. While the analysis of causal relationships is ideally done using experimental data\, in several research areas (e.g.\, social sciences) experiments are often infeasible or suffer from important limitations. As a result\, most empirical studies in the social sciences and related research areas are based on observational (i.e.\, nonexperimental) data.\nParticipants in this course will learn state-of-the-art methods used for investigating causal relationships with observational data. Course participants will also learn how to evaluate and discuss the appropriateness of research designs (“identification strategies”) and empirical methods for analysing causal relationships\, and they will learn to choose the most appropriate research designs and empirical methods for a specific research question. This will help participants obtain more credible and reliable results in their own research.\nTopics taught in this course include causal directed acyclic graphs (DAGs)\, methods based on ‘selection-on-observables’\, methods based on instrumental variables\, synthetic control methods\, regression discontinuity designs\, difference-in-differences\, methods for panel data with staggered treatment\, and causal machine learning methods. The course participants will learn the theoretical background and underlying assumptions of these methods as well as how to apply them in real-world analyses. \nLearning outcomes\nIntended learning outcomes for the students who complete the course: \nKnowledge\n• Understand causal DAGs.\n• Describe methods for causal inference with observational data\, including\n• ‘Selection-on-observables’\n• Instrumental variables\n• Synthetic control\n• Regression discontinuity designs\n• Difference-in-differences\n• Methods for panel data with staggered treatment\n• Causal machine learning\n• Describe the assumptions that need to be fulfilled if the methods listed above should give reliable estimates of causal effects. \nSkills\n• Construct and interpret causal DAGs and use them to identify causal effects using the DAGitty software.\n• Apply methods for causal inference with observational data using (statistical) software such as R\, Stata\, or Python.\n• Assess to which extent assumptions that are required by different causal inference methods with observational data are fulfilled in specific real-world applications. \nCompetences\n• Choose research designs and methods that are appropriate for causal inference with observational data in their research area.\n• Critically evaluate the appropriateness of research designs (“identification strategies”) and methods for answering causal questions with observational data in their research area (this refers to their own research\, e.g.\, when discussing strength and weaknesses of causal analyses in their own papers\, as well as to the research done by others\, e.g.\, when reviewing manuscripts or assessing the reliability of causal analyses). \nTarget Group\nPh.D. students at SCIENCE\, SUND\, SAMF\, and other faculties or universities\, who aim to investigate causal questions with observational data (e.g.\, economists\, other social scientists\, nutritionists\, epidemiologists\, other health/veterinary scientists\, etc.). \nRecommended Academic Qualifications\nThe students should have basic knowledge in statistics (e.g.\, hypothesis tests\, ordinary least-squares (OLS)\, etc.) obtained\, e.g.\, in the statistics variant of the PhD course “Fundamentals of the PhD education at SCIENCE – module 2” or a similar course. \nResearch Area\nAll research areas that apply statistical methods to answer causal research questions with observational data\, including economics\, other social sciences\, nutritional sciences\, epidemiology\, other health/veterinary sciences\, etc. \nTeaching and Learning Methods\nThe course participants are encouraged to read some of the course material before the course starts to be well prepared. The course consists of a combination of lectures and practical exercises. The participants will construct and interpret causal DAGs and they will learn to implement various methods for estimating causal effects. While the teachers will use the R software to present solutions to these exercises\, the participants are free to use other software (e.g.\, Stata or Python). The practical exercises also include group discussions\, e.g.\, about the appropriateness of research designs (“identification strategies”) and empirical methods. The course participants can choose to write a short report (5-10 pages)\, in which they apply at least one of the methods taught in the course to simulated or real-world observational data\, e.g.\, as a part of their PhD project. Reproducibility of the empirical analysis will play a key role in the lectures\, the practical exercises\, and in the ‘short report’ (exam). \nType of Assessment\nThe participants get this course approved with 2.5 ECTS if they attend the lectures\, do the practical exercises\, and pass a multiple-choice test given at the end of course.\nThe participants get this course approved with 5 ECTS if they additionally write and submit a short report (see above) that is positively assessed by the teachers. This short reported has to be submitted to the course coordinator no later than 3 months after the end of the course. \nLiterature\n• Angrist\, J.D. and Pischke\, J.-S. (2009)\, Mostly Harmless Econometrics\, Princeton University Press.\n• Angrist\, J. D. and Pischke\, J. S. (2014). Mastering ‘Metrics: The Path from Cause to Effect. Princeton University Press.\n• Bellemare\, M.F.\, Bloem\, J.R. and Wexler\, N. (2024): The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion. Oxford Bulletin of Economics and Statistics 86: 951-993. https://doi.org/10.1111/obes.12598\n• Didelez\, V. (2025): Causal Reasoning and Inference in Epidemiology. In Ahrens\, W. and Pigeot\, I: Handbook of Epidemiology\, Springer\, New York. https://doi.org/10.1007/978-1-4614-6625-3_74-1\n• Digitale\, J.C.\, Martin\, J.N. and Glymour\, M.M. (2022). Tutorial on directed acyclic graphs. Journal of Clinical Epidemiology\, 142\, pp.264-267.\n• Henningsen\, A.\, Low\, G.\, Wuepper\, D.\, Dalhaus\, T.\, Storm\, H.\, Belay\, D. and Hirsch\, S (2025): Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists. arXiv preprint. https://doi.org/10.48550/arXiv.2508.02310.\n• Hernán\, M.A. (2018). The C-word: scientific euphemisms do not improve causal inference from observational data. American journal of public health\, 108(5)\, pp.616-619.\n• Hernán & Robins (2020): Causal Inference: What If? Chapman & Hall/CRC\, Boca Raton (particularly chapters 1\, 2\, 3\, 4\, 6\, 7\, 8 & 16)\, https://miguelhernan.org/whatifbook\n• Huber\, M. (2023): Causal analysis: Impact evaluation and Causal Machine Learning with applications in R. MIT Press.\n• Huber\, M. (2025): Impact Evaluation in Firms and Organizations: With Applications in R and Python. MIT Press.\n• Morgan\, S.L. and Winship\, C. (2014)\, Counterfactuals and Causal Inference: Methods and Principles for Social Research\, 2nd ed. Cambridge University Press.\n• Tennant\, P.W.\, Murray\, E.J.\, Arnold\, K.F.\, Berrie\, L.\, Fox\, M.P.\, Gadd\, S.C.\, Harrison\, W.J.\, Keeble\, C.\, Ranker\, L.R.\, Textor\, J. and Tomova\, G.D. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International journal of epidemiology\, 50(2)\, pp.620-632.\n• Textor\, J. (2015)\, Drawing and analyzing causal DAGs with DAGitty. arXiv preprint arXiv:1508.04633 \nCourse coordinator\nArne Henningsen\, Associate Professor\, arne@ifro.ku.dk \nTeachers\nArne Henningsen\, Associate Professor\, arne@ifro.ku.dk\nBo Markussen\, bomar@math.ku.dk\nChristine Winther Bang\, cwb@math.ku.dk \nGuest Lecturers\nWhen we taught the course in 2025\, we invited two renowned experts to give guest lectures on two of the methods covered in this course\, respectively. As this worked very well\, we plan to include the same or similar guest lectures in the 2026 course as well. \nDates\n4-8 May 2026 – 9-17 all days. \nExpected frequency\nOnce per year in teaching block 4. \nCourse location\nUCPH Campus \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/estimating-causal-effects-with-observational-data/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260504T100000 DTEND;TZID=Europe/Copenhagen:20260504T170000 DTSTAMP:20260428T033321 CREATED:20260203T112728Z LAST-MODIFIED:20260204T090813Z UID:10001886-1777888800-1777914000@ddsa.dk SUMMARY:NAVIGATING THE COMPLEXITIES OF ULTRA-PROCESSED FOODS DESCRIPTION:Join us for a day of science and networking at Arla’s head office! \nTogether we will explore the topic of ultra-processed foods (UPFs) and navigate key questions – from what UPFs are and why processing matters to what current research suggests about potential health effects. \nThroughout the day\, you will connect with scientists\, researchers\, clinicians\, industry professionals\, and health advisors from multiple fields\, share perspectives\, and take part in conversations that shape where ultra-processed food research and discussion may head next. \nRead more and sign up at the DDEA website! URL:https://ddsa.dk/event/navigating-the-complexities-of-ultra-processed-foods/ LOCATION:Arla Foods Head Office\, Sønderhøj 14\, Viby J\, 8260\, Denmark CATEGORIES:Other Events ATTACH;FMTTYPE=image/jpeg:https://ddsa.dk/wp-content/uploads/2026/02/UPF.jpg ORGANIZER;CN="DDEA":MAILTO:ouh.ddea@rsyd.dk END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260504T113000 DTEND;TZID=Europe/Copenhagen:20260505T130000 DTSTAMP:20260428T033321 CREATED:20260216T065213Z LAST-MODIFIED:20260216T065213Z UID:10001923-1777894200-1777986000@ddsa.dk SUMMARY:Two-day Meeting in the Danish Society for Statistics DESCRIPTION:CLINDA – Center for Clinical Data Science – is pleased to announce that the programme for the 2026 Spring Two Day Meeting is now available\, and that registration is open. \nThe two day event features talks within:\n🔹 Biostatistics\n🔹 Representation Learning\n🔹 Bioinformatics\n🔹 Privacy Enhancing Techniques \nWe look forward to welcoming participants from across Denmark for two days of scientific exchange\, collaboration and inspiration. URL:https://ddsa.dk/event/two-day-meeting-in-the-danish-society-for-statistics/ LOCATION:Aalborg University\, Selma Lagerløfs Vej 249\, Selma Lagerløfs Vej 249\, Gistrup\, 9260\, Denmark CATEGORIES:DDSA-Funded Event END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260505 DTEND;VALUE=DATE:20260527 DTSTAMP:20260428T033321 CREATED:20260309T141529Z LAST-MODIFIED:20260309T141529Z UID:10001928-1777939200-1779839999@ddsa.dk SUMMARY:Statistical analysis of repeated measurements and clustered data DESCRIPTION:Enrolment guidelines \nThis is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. \nAnyone can apply for the course\, but if you are not a PhD student at the Graduate School\, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline\, available seats will be allocated to the waiting list. \nThe course is free of charge for PhD students at Danish universities (except Copenhagen Business School)\, and for PhD students at NorDoc member faculties. All other participants must pay the course fee \nCourse title\nStatistical analysis of repeated measurements and clustered data \nAim and study objectives: \nThis advanced statistics course will give you and introduction to the most common repeated measurement designs used in medical research. The aim of the course is to teach you to:\n• understand and interpret the analyses of various repeated measurement designs including baseline follow-up studies\, cross-over trials\, and reproducibility of measurement methods\, as well as analyses of clustered designs (e.g. multi-level models)\, and of mixed type.\n• perform your own analyses using R statistical software.\n• use model diagnostics to assess the validity of your analyses.\n• make suitable presentations of the results from your analyses.\n• understand the statistical consequences of different kinds of study designs. \nContent\nThis course is concerned with the analysis of correlated quantitative outcomes data arising e.g. when collecting data repeatedly on the same persons\, animals\, or tissue over time or on different locations of the body\, or when observations are clustered as from patients in a multi-center study\, siblings or pups belonging to the same litter. Appropriate statistical models for analysis will be exemplified and statistical errors arising with other frequently employed analyses will be discussed. Topics include analysis of baseline follow-up studies\, longitudinal data analysis\, multi-level and variance component models\, analysis of cross-over trials\, and reproducibility of measurements methods. We will further discuss the potential biases that occur due to missing data and statistical methods for handling these. A thorough introduction to linear mixed models for quantitative outcomes will be given\, while generalized linear mixed models and generalized estimating equations for the analysis of binary outcomes are more briefly touched upon the last day of the course. Computer exercises with R statistical software will be given. \nStatistical software\nYou must bring your own labtop with R and R Studio installed to participate in the exercise classes. Note that if you have never used R before we strongly recommend that you complete a course on R programming before attending this course. \nTextbook\nMany of the analyses taught are covered by G.M. Fitzmaurice\, N.M. Laird\, & J.H. Ware. Applied Longitudinal Analysis\, 2nd ed.\, John Wiley & Sons\, 2011. You are not required to buy the book\, but we recommend it for further reading. Note: Students at the University of Copenhagen have free access to the e-book through the Royal Library. Lecture notes and R-demos are available from the course webpage. \nParticipants\nPh.D.-students with a basic knowledge of statistics\, e.g. corresponding to the course “Basic statistics for health researchers” and R programming at beginner level. In case of vacant seats also other health researchers. Max. 70 participants. \nRelevance to graduate programmes\nThe course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences\, UCPH. \nALL GRADUATE PROGRAMMES \nLanguage\nEnglish. \nForm\n6 full days with forum lectures and computer exercises. Participating in at least four of the six exercise classes is required to pass the course. \nCourse director\nAssociate professor Julie Lyng Forman\, Department of Biostatistics \nTeachers\nAssociate professor Julie Lyng Forman\, associate professor Brice Ozenne and others. \nCourse secretary\nSusanne Kragskov Laupstad\, Department of Biostatistics\, e-mail: skl@sund.ku.dk \nDates\n5\, 8\, 12\, 19\, 22\, 26 May 2026\, all days 8-15. \nCourse location\nCSS \nRegistration: Please register before 1 April 2026. \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 last day of enrolment. \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student\, your participation in the course must be in agreement with your principal supervisor. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/statistical-analysis-of-repeated-measurements-and-clustered-data-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260506 DTEND;VALUE=DATE:20260508 DTSTAMP:20260428T033321 CREATED:20260420T110410Z LAST-MODIFIED:20260420T110410Z UID:10001949-1778025600-1778198399@ddsa.dk SUMMARY:Empirical Finance: Identification Strategies in Corporate Finance DESCRIPTION:Prerequisites \nThe course is designed as a first-year Ph.D. course. The prerequisites are knowledge of corporate finance theory and econometrics at a M.Sc. level and an ability to work independently with data using a statistical program such as Stata. \nStudents must participate in the whole course and do all problem sets. \nAim and Objective \nThe aim of the class is to introduce PhD students in finance and related fields to identification strategies in corporate finance. \nThe course is designed to provide an introduction to some of the empirical\nmethods used to identify causal effects in corporate finance. \nWe will examine how to estimate causal effects in the presence of\npotentially unobserved confounding factors and how to make proper\nstatistical inference about empirical estimates. \nThe goal of the course is to provide PhD students with a methodological\nframework that will enhance their ability to design sound identification\nstrategies in the area of corporate finance. \nCourse content \nThe course is designed to provide an introduction to some of the empirical methods used to identify causal effects in corporate finance. We will examine how to estimate causal effects in the presence of potentially unobserved confounding factors and how to make proper statistical inference about empirical estimates. \nThe goal of the course is to provide PhD students with a methodological framework that will enhance their ability to design sound identification strategies in the area of corporate finance. \nThe course content has three main elements:\n1. The students will be introduced to the main empirical methods used to identify causal effects in corporate finance. The lectures covers the main econometric challenges as well as guidance on how to estimate causal effects.\n2. The course combines lectures on microeconometrics with lectures on seminal papers that apply the empirical methods to research questions in the area of corporate finance.\n3. The course has a two problem sets that students must complete. \nFor further information and registration please follow the link to the CBS course page. \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/empirical-finance-identification-strategies-in-corporate-finance-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260506 DTEND;VALUE=DATE:20260509 DTSTAMP:20260428T033321 CREATED:20260204T133532Z LAST-MODIFIED:20260420T110447Z UID:10001787-1778025600-1778284799@ddsa.dk SUMMARY:Advanced Optimization Techniques for Energy Systems Planning and Operation DESCRIPTION:Welcome to Advanced Optimization Techniques for Energy Systems Planning and Operation \nDescription: Optimal decision-making is a must in energy system planning and operation as the non-optimal decisions may lead to high economic losses and/or technical issues. The course on “Advanced Optimization Techniques for Energy Systems Planning and Operation” is aimed at providing an in-depth introduction to energy system optimization methods. The course will contain a wide range of the basic methods to advanced techniques with hand on examples related to energy systems. The participants will learn how to implement the methods using optimization packages such as GAMS\, Python\, and MATLAB. \nFor additional information\, updates\, and registration\, please refer to AAU PhD Moodle via the link provided on the right side of this page. \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/advanced-optimization-techniques-for-energy-systems-planning-and-operation/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260511 DTEND;VALUE=DATE:20260514 DTSTAMP:20260428T033321 CREATED:20260420T110538Z LAST-MODIFIED:20260427T105642Z UID:10001888-1778457600-1778716799@ddsa.dk SUMMARY:Wireless Communicatons for the Internet of Things (IoT) DESCRIPTION:Welcome to Wireless Communicatons for the Internet of Things (IoT) \nDescription:\nThe rapid proliferation of Internet of Things (IoT) applications is transforming both daily life and industrial operations\, creating unprecedented demands on wireless communication systems. IoT devices exhibit diverse characteristics\, communication requirements\, and traffic patterns that challenge traditional network architectures and protocols. To meet these demands\, wireless communication systems must evolve towards self-optimization and self-adaptation\, leveraging artificial intelligence (AI) and data-driven methods.This PhD course provides an in-depth review of: \n\nThe theoretical foundations for designing efficient access protocols for wireless IoT;\nThe challenges and opportunities of IoT-enabling technologies;\nMachine learning and data-driven techniques for enhanced IoT connectivity;\nEmerging communication architectures and infrastructures for IoT\n\nIn addition to lectures and discussions\, the course features a practical session where students gain hands-on experience with NB-IoT data collection and analysis \nFor additional information\, updates\, and registration\, please refer to AAU PhD Moodle via the link provided on the right side of this page. \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/wireless-communicatons-for-the-internet-of-things-iot/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260511 DTEND;VALUE=DATE:20260514 DTSTAMP:20260428T033321 CREATED:20260420T111748Z LAST-MODIFIED:20260420T111748Z UID:10001909-1778457600-1778716799@ddsa.dk SUMMARY:Introduction to bulk RNAseq analysis DESCRIPTION:Enrolment guidelines \nThis is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. \nAnyone can apply for the course\, but if you are not a PhD student at the Graduate School\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to the waiting list. \nThe course is free of charge for PhD students at Danish universities (except Copenhagen Business School)\, and for PhD students at NorDoc member faculties. All other participants must pay the course fee. \nLearning objectives\nA student who has met the objectives of the course will be able to: \n1. Gain insight into how to design an RNA-seq experiment \n2. Preprocess sequencing reads \n3. Analyze bulk RNAseq data using the R package DESeq2 \n4. Know best practices for performing Differential Expression Analysis \n5. Annotate and interpret their results \nContent\nThis course is an introduction for how to approach bulk RNAseq data\, starting from the sequencing reads. It will provide an overview of the fundamentals of RNAseq analysis\, including read preprocessing\, data normalization\, data exploration with PCAs and heatmaps\, performing differential expression analysis and annotation of the differentially expressed genes. Participants will also learn how to evaluate confounding and batch effects in the data. The course will further touch upon laboratory protocols\, library preparation\, and experimental design of RNA sequencing experiments\, especially about how they influence downstream bioinformatic analysis. \nParticipants\nThe course is intended for PhD students at SUND who are interested in learning how to treat their bulk RNAseq data. The course is specifically targeted towards medical and biological researchers who are looking to strengthen their bioinformatics analysis skills\, though PhD students from all disciplines are welcome. \nRequirements\n– Working knowledge of the command line\, R language\, RStudio and Rmarkdown is mandatory. \n– Basic knowledge of RNA sequencing technology. \n– Basic knowledge of data science concepts such as principal component analysis\, clustering and statistical testing. \nRelevance to graduate programmes\nThe course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences\, UCPH: \n– Biostatistics and Bioinformatics \n– Molecular Mechanisms of Disease \n– Veterinary\, Animal Health and Microbiological Sciences \n– All graduate programmes \nLanguage\nEnglish \nForm\nThis course is a 3-day course composed of a combination of lectures and exercise sessions. \nCourse director\nAnders Krogh\,?\nProfessor\, Head of Center for Health Data Science\,?\nCenter for Health Data Science\,?\nanders.krogh@sund.ku.dk \nTeachers\nAlba Refoyo Martinez\nPhD\, Sandbox Data Scientist\,\nCenter for Health Data Science\,\nalba.martinez@sund.ku.dk \nThilde Terkelsen\nPhD\, Special Consultant\,\nCenter for Health Data Science\,\nthilde.terkelsen@sund.ku.dk \nSuze Roostee\nPhD\, Data Scientist\nCenter for Health Data Science.\nsuze.roostee@sund.ku.dk \nDiana Andrejeva\,\nPhD\, Special Consultant\,\nCenter for Health Data Science\,\nandrejeva@sund.ku.dk \nDates and location\n11 May in Holst \n12 May in Holst \n13 May in Haderup \nFaculty of Health and Medical Sciences\, Panum\,?\nBlegdamsvej 3B\, 2200 København. \nExpected frequency\nWe plan to run this course again in 2027. \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 last day of enrolment. \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student\, your participation in the course must be in agreement with your principal supervisor. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/introduction-to-bulk-rnaseq-analysis-3/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260521 DTEND;VALUE=DATE:20260523 DTSTAMP:20260428T033321 CREATED:20260420T130520Z LAST-MODIFIED:20260427T114715Z UID:10001833-1779321600-1779494399@ddsa.dk SUMMARY:HPC Launch DESCRIPTION:Enrolment guidelines \nThis is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. \nAnyone can apply for the course\, but if you are not a PhD student at the Graduate School\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to the waiting list. \nThe course is free of charge for PhD students at Danish universities (except Copenhagen Business School)\, and for PhD students at NorDoc member faculties. All other participants must pay the course fee. \nLearning objectives \nBy the end of the course\, participants will be able to: \nUnderstand HPC systems\n• Explain the purpose\, structure\, and key components of HPC platforms.\n• Identify suitable HPC resources and storage for different projects. \nSet up computing environment essentials\n• Use terminal commands\, SSH\, terminal multiplexers (e.g.\, tmux)\, and IDEs (like Visual Studio Code) effectively.\n• Track changes using version control with Git for collaboration (e.g.\, GitHub).\n• Manage software and data analyses via best practices (e.g.\, conda)\n• Execute computations by submitting and managing jobs with scheduler systems (e.g.\, SLURM). \nManage computational projects with a research data management (RDM) focus\n• Plan for compute and storage resource needs for a project’s lifetime.\n• Plan and structure projects with organized files and templates (e.g.\, Cookiecutter).\n• Document projects by following metadata standards for reproducibility and reuse (e.g.\, ontologies).\n• Manage storage\, archive projects and optimize HPC storage use. \nContent \nThe goal of HPC-Launch is to equip researchers with the skills to start or reconfigure biological/health data projects using modern computing and research data management practices. The course consists of two integrated modules:\n• High-Performance Computing (HPC): Develop a practical understanding of HPC platforms\, covering system structure\, job scheduling\, and tools for efficient interaction (e.g.\, SSH\, tmux\, IDEs). The module bridges technical gaps for beginner to intermediate users and introduces computing resources available to Danish researchers. \n• Research Data Management (RDM): Highlight the importance of good RDM practices and provide hands-on strategies for structuring projects\, documenting data and metadata\, applying version control\, and archiving results for collaboration and reuse. \nParticipants will gain:\n• Confidence in navigating HPC systems and running analyses efficiently\n• Tools and practices for managing files\, projects\, and data reproducibly\n• Experience applying concepts through exercises on the UCloud HPC platform (SDU) using bash and relevant IDEs \nIn summary\, HPC-Launch focuses on the efficient use of HPC resources for complex health data science projects\, while also providing an overview of best practices in research data management. The course combines theory\, case discussions\, and hands-on exercises to strengthen participants’ ability to design efficient projects\, conduct large-scale analyses\, and ensure the long-term usability and reproducibility of data and results. \nHPC-Launch is the first course of a series we are developing to elevate practical technical skills in health data science\, and is the intended prerequisite for HPC-Pipes (focused on environment management and bioinformatics pipeline languages). \nParticipants \nSeats: 25 \nTarget trainee \nResearchers and students who mainly rely on their own laptops for analysis\, have little experience with HPC\, or feel unsure about working directly on servers\, but have large datasets / sensitive data / resource-intensive tools to handle in their research. They seek a more professional and reproducible environment for their analyses but may lack confidence or familiarity with command-line workflows and remote computing systems.\nRequirements \nThe workshop is for PhD students and researchers at SUND who seek to acquire skills in effectively managing data and computing projects. The course is targeted to students performing data analysis on biological data or within the field of data science. Prior computational experience is required – we expect you to be comfortable independently programming in RStudio or Jupyter Lab before you sign up for the course. Basic knowledge of bash is also highly recommended for the demo session. \nLaptops will be required for all hands-on exercises. If you cannot easily install software on your laptop or lack administrative rights\, please let the course organizers know when you sign up. \nRelevance to graduate programs \nThe course is relevant to PhD students from the following graduate programs at the Graduate School of Health and Medical Sciences\, UCPH:\nBiostatistics and Bioinformatics \nBasic Metabolic Research \nAll graduate programmes \nLanguage \nEnglish \nForm \nLectures with active discussion sessions\, interactive demos using the UCloud platform\, and group work and exercises navigating UCloud and practicing with tools for RDM-compliant project set-up. \nCourse director \nAnders Krogh\,\nProfessor\, Head of Center for Health Data Science\, Head of Health Data Science Sandbox\nCenter for Health Data Science\,\nanders.krogh@sund.ku.dk \nTeachers \nThe workshop is provided by project members of the Health Data Science Sandbox\, a national training and research infrastructure project. The Sandbox team is building training resources and guides for learning bioinformatics\, predictive modeling in precision medicine\, high performance computing and data carpentry. These resources are accessible to all Danish university employees (PhD students and up) via academic supercomputing infrastructure. \nJennifer Bartell\nPhD\, Senior consultant and Sandbox project manager\nCenter for Health Data Science\, KU\nbartell@sund.ku.dk \nAlba Refoyo Martinez\nPhD\, Data Scientist\, Sandbox Team\nCenter for Health Data Science\, KU\nalba.martinez@sund.ku.dk \nDates \n21-22 May 2026 \nCourse location \nFaculty Club 16.6.16\nFaculty of Health and Medical Sciences\, Panum\,\nBlegdamsvej 3B\, 2200 København. \nRegistration \nPlease register by 23 April 2026 \nExpected frequency \nThis course will likely be repeated in Spring 2027. \nSeats 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 last day of enrollment. \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student\, your participation in the course must be in agreement with your principal supervisor. \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/hpc-launch-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260521T120000 DTEND;TZID=Europe/Copenhagen:20260521T160000 DTSTAMP:20260428T033321 CREATED:20260210T065919Z LAST-MODIFIED:20260210T065919Z UID:10001915-1779364800-1779379200@ddsa.dk SUMMARY:Mini Omics series of workshops DESCRIPTION:I’m excited to invite you to our four workshop in our OMICS mini-series\, where we’ll kick things off with R & the Tidyverse 🧬📊 \nThe first session is all about getting comfortable with R and tidy tools for data manipulation and visualization — skills you’ll use again and again in the following workshops about bulk RNA-seq\, single-cell\, and spatial transcriptomics. The in-person meeting Venue is in the Aarhus university main campus\, but we can stream the lectures as well. \n🗓 When and where: 1) February 26\, 12:00–16:00\, @ Bartholin building (1241-114) Tidy R\n2) March 26\, 12:00–16:00\, @ Bartholin building (1241-114) bulkRNA\n3) April 23\, 12:00–16:00\, @ 1264-104 single cell RNA\n4) May 21\, 12:00–16:00\, @ Bartholin building (1241-114) spatial transcriptomics\n🧑‍💻 Format: Hands-on\, beginner-friendly\, with help during exercises (We’ll stream the talks too) \nWho’s it for?\nEveryone! Complete beginners are very welcome\, and more experienced R users should still find it useful. \nThere’ll be cake + warm drinks ☕🍰\, optional take-home exercises\, and you can also follow up with questions at the ABC coding café (https://abc.au.dk/). \nSignup: 1) https://forms.gle/aN6PbyNsgGyyx4VD6 Tidy and R\n2) https://forms.gle/hEUYh3uNBiASzAFf6 bulkRNA\n3) https://forms.gle/sYvCu5sgpLJTtHSk7 single cell RNA\n4) https://forms.gle/FCu4JeKMbRUaAfkN9 spatial transcriptomics \nHope to see many of you there! 😊 URL:https://ddsa.dk/event/mini-omics-series-of-workshops/2026-05-21/ LOCATION:Aarhus University\, Aarhus C CATEGORIES:DDSA-Funded Event,Other Events ATTACH;FMTTYPE=image/jpeg:https://ddsa.dk/wp-content/uploads/2026/02/images.jpg ORGANIZER;CN="Health Data Science Sandbox%2C Core Bioinformatics Facility%2C Aarhus University":MAILTO:samuele@birc.au.dk END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260527 DTEND;VALUE=DATE:20260530 DTSTAMP:20260428T033321 CREATED:20260427T115028Z LAST-MODIFIED:20260427T115028Z UID:10001824-1779840000-1780099199@ddsa.dk SUMMARY:Explanatory modeling of observational quantitative data: Causal graphs\, interactions\, and research design choices DESCRIPTION:Welcome to Explanatory modeling of observational quantitative data: Causal graphs\, interactions\, and research design choices \nDescription: \n“Explanatory modeling of observational quantitative data ” is an applied course designed for PhD students in quantitative social sciences who wish to deepen their understanding and skills in working with observational quantitative data (e.g.\, non-experimental data). Causality is hard to establish with observational data\, yet a theory-testing approach still forces researchers to formulate causal theoretical models to motivate their statistical choices. This course works with this tension in quantitative social science research and engages with applied recommendations for best practice. \nThe first day of the course introduces directed acyclic graphs (DAGs)\, discusses the need for causal reasoning for regression-based modeling\, and offers training for developing DAGs. \nThe second day is dedicated to further complexities emerging from interactive and non-linear hypotheses\, as well as mediation relationships\, introducing statistical packages to ensure valid inferences for these statistical scenarios. \nThe third day introduces approaches for ‘causal’ research designs to observational data\, focusing on matching estimators and different regression-based approaches (e.g.\, instrumental variables). We compare these to regression-based approaches and discuss strengths and weaknesses. We end the third day with a Lab session that provides the chance to apply some of the content to your own research. \nThe course expects a basic familiarity with quantitative methods (e.g.\, linear regression). The applied statistical teaching is done with R\, and students are recommended to have basic knowledge of R programming. Most of the course content\, however\, can also be followed with STATA (e.g.\, similar/same packages in STATA). Students are encouraged to bring their own research questions to the course and engage in potential inferential/modeling challenges in their field during the practical parts of the course. \nThrough a combination of lectures\, practical exercises\, and case studies\, students will engage with current best practices in observational quantitative social science research. You will learn about the use of directed acyclic graphs. You will explore various statistical tools\, such as kernel or bin plots of linear interactive relationships\, as well as approaches for causal mediation analysis. Finally\, you will be enabled to make informed choices of whether matching or regression-based approaches might be useful tools for your analysis. \nBy the end of the course\, you will have a comprehensive toolkit of advanced statistical approaches to tackle complex research questions in quantitative social sciences. You will also gain the ability to critically evaluate existing literature and design rigorous empirical studies using observational data. \nTeaching methods: \n\nLectures\, practical exercises\, and case studies\nApplied programming with R (Lab Sessions)\nIllustration of the statistical approaches with real world cases and data\nOpportunities to work with your own data during the course\n\nFor additional information\, updates\, and registration\, please refer to AAU PhD Moodle via the link provided on the right side of this page. \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/explanatory-modeling-of-observational-quantitative-data-causal-graphs-interactions-and-research-design-choices/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260528 DTEND;VALUE=DATE:20260530 DTSTAMP:20260428T033321 CREATED:20260427T115049Z LAST-MODIFIED:20260427T115049Z UID:10001844-1779926400-1780099199@ddsa.dk SUMMARY:Knowledge graph construction DESCRIPTION:Welcome to Knowledge graph construction \nDescription: The ability to represent and reason with knowledge in a structured\, machine-readable format is rapidly becoming essential for modern artificial intelligence applications. Knowledge graphs (KGs) offer a powerful paradigm for achieving this\, enabling applications ranging from intelligent search and question answering to drug discovery and personalized medicine. However\, constructing high-quality KGs is a complex undertaking\, requiring expertise in information extraction\, entity resolution\, and data integration – challenges that lie at the intersection of knowledge engineering\, information retrieval\, natural language processing\, and increasingly\, domain-specific knowledge.\nThis course addresses the problem of building robust knowledge graphs from diverse data sources. The course will start with a comprehensive introduction to the principles and techniques underlying KG construction\, starting from the basic KG concepts\, (e.g. data models and query languages)\, and continuing with advanced topics such as information extraction from free text\, entity matching\, and KG ingestion. The course will also discuss approaches based on large language models (LLMs) for automating the extraction of structured data from text. As a use-case to illustrate the application of the notions and techniques\, the course will consider real-world applications within the medical domain. \nFor additional information\, updates\, and registration\, please refer to AAU PhD Moodle via the link provided on the right side of this page. \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/knowledge-graph-construction/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260528T083000 DTEND;TZID=Europe/Copenhagen:20260529T150000 DTSTAMP:20260428T033321 CREATED:20260203T112915Z LAST-MODIFIED:20260204T090831Z UID:10001887-1779957000-1780066800@ddsa.dk SUMMARY:NEUROENDOCRINE CONTROL OF ENERGY METABOLISM: PATHWAYS TO THERAPY DESCRIPTION:Join us for a two-day symposium that explores the critical interface between neuroscience and endocrinology! \nThe program will examine the brain’s central role in regulating metabolic balance\, with a particular focus on its influence on health and its implications in conditions such as obesity and type 2 diabetes. You will gain a deeper understanding of how alterations in brain function can increase susceptibility to cardiometabolic disorders\, as well as new perspectives on how peripheral signals affect the brain’s ability to maintain energy homeostasis. Together\, these insights will shed light on mechanisms of disease and point toward potential avenues for future therapeutic strategies. \nSpecifically\, scientific sessions include: \n– Endocrine Circuits Across the Gut\, Brain\, and Adipose Axis\n– Short Talks Selected from Submitted Abstracts\n– Circuits Controlling Food Intake – Food Motivation and Reward\n– Central Regulation of Energy Balance and Metabolism\n– Emerging Technologies to Identify Novel Energy-Homeostatic Circuits\n– Short Talks Selected from Submitted Abstracts\n– Poster presentations \nYou are invited to present your research at the symposium. Short talks\, posters and poster presetations will be arranged based on selected abstracts. All who have signed up with an abstract will present their research in an open poster session. More information will follow after the registration deadline. \nThere will also be additional\, excellent opportunities to expand your professional network and seek out possible future collaborators within different scientific fields as well as across national and international borders. \nThe event is jointly organised by Danish Diabetes and Endocrine Academy (DDEA) and Neuroscience Academy Denmark (NAD)\, and registration is via DDEA’s website. URL:https://ddsa.dk/event/neuroendocrine-control-of-energy-metabolism-pathways-to-therapy/ LOCATION:Mærsk Tower\, University of Copenhagen\, Blegdamsvej 3B\, Copenhagen\, 2200\, Denmark CATEGORIES:Other Events ATTACH;FMTTYPE=image/jpeg:https://ddsa.dk/wp-content/uploads/2026/02/Neuroendocrine-Control-of-Energy-Metabolism-Pathways-to-Therapy.jpg ORGANIZER;CN="DDEA":MAILTO:ouh.ddea@rsyd.dk END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260528T150000 DTEND;TZID=Europe/Copenhagen:20260528T170000 DTSTAMP:20260428T033321 CREATED:20260324T093037Z LAST-MODIFIED:20260324T093037Z UID:10001951-1779980400-1779987600@ddsa.dk SUMMARY:Talking HeaDS – Health Data Science Seminar and Social Networking DESCRIPTION:“This must be the place” to hear about the latest and greatest in the expanding field of health data science. The mission of the Center for Health Data Science is to not only strengthen health data science within the Faculty of Health and Medical Sciences (SUND) at the University of Copenhagen\, but to serve as a hub for researchers using health data science. \nThe Talking HeaDS seminar will be presented by Helene Charlotte Wiese Rygtaard. \n“One of the key challenges in medical research consists of analyzing the effects of treatments administered over time using real-world data.” \nHelene works on methodological research in the areas of causal inference\, targeted (machine) learning\, event history analysis\, and efficient nonparametric estimation. Her main focus is on the development of statistical machine learning methods for estimating intervention effects in time-varying settings\, and their appropriate application in medical and epidemiological studies. \nThe Talking HeaDS seminar series offers:\nPresentations on research developments across disciplines that leverage data science methodologies\nQuestion and answer sessions with researchers\nNetworking\, mingling\, and refreshments to strengthen relationships across health data science URL:https://ddsa.dk/event/talking-heads-health-data-science-seminar-and-social-networking-5/ CATEGORIES:DDSA-Funded Event ATTACH;FMTTYPE=image/png:https://ddsa.dk/wp-content/uploads/2026/03/1100x600_Course_Image_Talking_HeaDS.png ORGANIZER;CN="Center for Health Data Science (HeaDS)":MAILTO:heads-admin@sund.ku.dk END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260529 DTEND;VALUE=DATE:20260602 DTSTAMP:20260428T033321 CREATED:20260427T115127Z LAST-MODIFIED:20260427T115201Z UID:10001828-1780012800-1780358399@ddsa.dk SUMMARY:From Excel to R DESCRIPTION:Enrolment guidelines \nThis is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. \nAnyone can apply for the course\, but if you are not a PhD student at the Graduate School\, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline\, available seats will be allocated to the waiting list. \nThe course is free of charge for PhD students at Danish universities (except Copenhagen Business School)\, and for PhD students at NorDoc member faculties. All other participants must pay the course fee. \nLearning objectives\nA student who has met the objectives of the course will be able to: \n1. Define and explain the use of basic programming concepts in the scripting language R.\n2. Perform data wrangling/data management tasks using the tidyverse framework.\n3. Visualize data and results of analysis with the ggplot2 package.\n4. Understand the use of tidyverse and ggplot2 in the context of applied statistics/bioinformatics in R.\n5. Demonstrate best practice when working in R\, importantly in regard to generating reproducible scientific results with R Markdown. \nContent\nThe course From Excel to R will spend time on making participants familiar with Rstudio\, directory paths\, scripts\, R-projects\, help functions\, shortcuts\, etc.\, to ensure that the users will have a solid understanding of the practicals before diving into using the scripting language itself. The course is taught mainly using the R-framework tidyverse\, a variety of R-functions and syntax which make data-wrangling much more intuitive for first time R-users. Tidyverse will form the basis for the other major focal point of the course\, that is\, plotting with ggplot2. \nNext\, the course will allow participants to test out their newly acquired skills in data wrangling and visualization on an example of basic applied statistics in R\, simultaneously introducing them to the strength of R as a statistical programming language. Participants will also briefly be introduced to R Markdown for reproducible report making in R. \nThe learning outcome of the course is to provide attendees with the basic skills for manipulating and setting up data for visualization and analysis in R. \nParticipants\nSeats: 35\nFrom Excel to R is an introductory course targeted towards people with no (or very limited) experience in R. \nRelevance to graduate programmers\nThe course is relevant to PhD students from the following graduate programs at the Graduate School of Health and Medical Sciences\, UCPH: \nAll graduate programs \nLanguage\nEnglish \nForm\nLectures\, interactive presentations from within R\, group work and exercises.\nN.B: Before the course starts participants must have installed the newest versions of R & Rstudio\, as well as a list of packages provided by instructors\, this is done to alleviate any installation issues on the course days. Anyone with installation issues can join for a technical help session on the first day of the course between 08:30 – 09:00. \nCourse director\nAnders Krogh\,\nProfessor\, Head of Center for Health Data Science\,\nCenter for Health Data Science\,\nanders.krogh@sund.ku.dk \nTeachers\nThilde Terkelsen\nPhD\, Data Scientist\nCenter for Health Data Science\nthilde.terkelsen@sund.ku.dk \nChelsea Lennox\nData Scientist\nCenter for Health Data Science\nchelsea.lennox@sund.ku.dk \nDiana Andrejeva\,\nPhD\, Special Consultant\,\nCenter for Health Data Science\,\nandrejeva@sund.ku.dk \nSuze Roostee\nPhD\, Data Scientist\nCenter for Health Data Science\nSuze.roostee@sund.ku.dk \nDates\n29 May & 1 June 2026 – 08:00 – 16:00 \nCourse location\nFaculty Club 16.6.16\nFaculty of Health and Medical Sciences\, Panum\,\nBlegdamsvej 3B\, 2200 København \nRegistration\nPlease register before 1 May 2026. \nExpected frequency\nThe course will run again in Fall 2026. \nSeats 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 last day of enrolment. \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student\, your participation in the course must be in agreement with your principal supervisor. \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/from-excel-to-r-5/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260601 DTEND;VALUE=DATE:20260606 DTSTAMP:20260428T033321 CREATED:20260427T115216Z LAST-MODIFIED:20260427T115216Z UID:10001890-1780272000-1780703999@ddsa.dk SUMMARY:Statistical learning theory for time-series DESCRIPTION:Welcome to Statistical learning theory for time-series \nDescription: \nThis course provides a comprehensive introduction to the theory\, algorithms\, and statistical guarantees for learning dynamical systems and performing time-series prediction. The course combines classical modeling techniques with modern deep learning architectures\, addressing both the estimation of unknown physical quantities and the construction of predictive models for applications such as forecasting the yearly energy consumption of buildings. \nThe theoretical foundation lies at the intersection of system identification\, econometrics\, statistics\, and machine learning. Students will study both linear models and nonlinear sequence models\, including: \n\nAutoregressive and state-space models\nParameter estimation and subspace methods\nRecurrent Neural Networks (RNNs)\nTransformers for sequential data\nDeep Structured State-Space Models (SSMs)\, including Mamba and related architectures architectures\n\nIn addition to understanding these models\, the course emphasizes statistical performance guarantees\, covering: \n\nAsymptotic consistency for learning linear autoregressive and state-space models\nFinite-sample error bounds for algorithms based on linear regression and subspace methods\nProbably Approximately Correct (PAC) and PAC-Bayesian guarantees for learning sequential models\, including deep architecture\n\nThe course is conducted as an intensive lecture series with physical attendance. Evaluation will be based on attendance and homework assignments. \nModern time-series prediction increasingly relies on deep learning methods such as RNNs\, transformers\, and structured state-space models\, yet these models are often used as black boxes without rigorous performance understanding. This course bridges classical dynamical systems theory with modern sequence modeling\, providing both practical tools and theoretical foundations to evaluate when and why these methods work\, and under what statistical guarantees they can be trusted. \nFor additional information\, updates\, and registration\, please refer to AAU PhD Moodle via the link provided on the right side of this page. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/statistical-learning-theory-for-time-series/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260601 DTEND;VALUE=DATE:20260612 DTSTAMP:20260428T033321 CREATED:20260427T120022Z LAST-MODIFIED:20260427T120022Z UID:10001969-1780272000-1781222399@ddsa.dk SUMMARY:Fundamentals in Computational Analysis of Large-Scale Datasets DESCRIPTION:Enrolment guidelines \nThis is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. \nAnyone can apply for the course\, but if you are not a PhD student at the Graduate School\, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline\, available seats will be allocated to the waiting list. \nThe course is free of charge for PhD students at Danish universities (except Copenhagen Business School)\, and for PhD students at NorDoc member faculties. All other participants must pay the course fee \n Learning objectives \nA student who has met the objectives of the course will be able to: \n1. Import\, visualize\, transform and summarize datasets using the R statistical programming language\n2. Be familiar with tools to create reproducible and scalable data analysis workflows\n3. Describe basic concepts in probability theory\n4. Distinguish supervised and unsupervised statistical learning and their applications\n5. Perform a comprehensive exploratory analysis on a given real-world data \n Content \nThe topic of this course is to provide the attendees with a broad introduction into the fundamentals of modern computational data analysis. The aim is to equip the attendees with the basic tools for “making sense of data”\, from the fundamentals of working with large-scale datasets to introductory probability theory and statistics. The first week of the course is dedicated to practical aspects of computational data analysis using the UNIX shell\, python and the R statistical programming language. Topics include data visualization and data wrangling as well as reproducible computational workflows. In the second week\, students will be introduced to basic concepts in probability theory and statistics\, with topics including a probability theory bootcamp; introduction to supervised learning (linear regression); and introduction to unsupervised learning (PCA). The students will learn these topics through a combination of introductory lectures and hands-on analysis examples on real-world datasets. \n Participants \nPhD fellows in the “Life\, Earth and Environmental Sciences” Programme (required course) or related fields where quantitative data analysis skills are requirements. \n Relevance to graduate programmes \nThe course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences\, UCPH: \nLife\, Earth and Environmental Sciences \nBiostatistics and Bioinformatics \nOral Sciences\, Forensic Medicine and Bioanthropology \n Language \nEnglish \n Form \nCombination of lectures and practical computational exercises For instance: Lectures\, group work\, \n Course director \nMartin Sikora\, Associate Professor\, Globe Institute\, University of Copenhagen.\nmartin.sikora@sund.ku.dk \n Teachers \nMartin Sikora\, Associate Professor\, Globe Institute\, University of Copenhagen.\nmartin.sikora@sund.ku.dk \nShyam Gopalakrishnan\, Associate Professor\, Globe Institute\, University of Copenhagen\, shyam.gopalakrishnan@sund.ku.dk \nAntonio Fernandez Guerra\, Assistant Professor\, Globe Institute\, University of Copenhagen\nantonio.fernandez-guerra@sund.ku.dk \n Dates \nBlock 4\, 2 weeks from 1/6/26 – 11/6/26. Monday-Thursday 9:00 – 16:00 \n Course location \nTeaching room Kommunehospital \n Registration \nPlease register before 10/05/26 \n Expected frequency \nAnnual \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 last day of enrolment. \nNote: All applicants are asked to submit invoice details in case of no-show\, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student\, your participation in the course must be in agreement with your principal supervisor. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk. URL:https://ddsa.dk/event/fundamentals-in-computational-analysis-of-large-scale-datasets-2/ CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260610T100000 DTEND;TZID=Europe/Copenhagen:20260610T160000 DTSTAMP:20260428T033321 CREATED:20260414T083022Z LAST-MODIFIED:20260414T083022Z UID:10001954-1781085600-1781107200@ddsa.dk SUMMARY:National Reproducibility & Research Data Event: What matters to researchers and research support? DESCRIPTION:How do we work with reproducibility in practice – and how can reproducible research be strengthened and scaled across disciplines?\nJoin a national full‑day event bringing together researchers and research support to share experiences\, challenges\, and solutions related to reproducibility and research data. \nWHAT TO EXPECT \nSession 1: Lightning talks & posters\nResearchers and research support staff are invited to present short talks and posters showcasing concrete reproducibility practices\, tools\, workflows\, and initiatives from their own work.\nThe session highlights both successes and challenges and provides ample opportunity for discussion\, inspiration\, and networking across disciplines and career stages. \nSession 2: Workshop – Reproducibility and me?\nAn interactive\, hands‑on workshop with group work and plenary discussion. Participants will:\n– assess current reproducibility practices within their own projects\, teams\, or research environments\n– exchange ideas on future priorities and directions for reproducible research\n– identify practical enablers to support and scale up reproducibility \nThe workshop is facilitated using the KE Reproducibility Framework\, developed through the European partnership Knowledge Exchange. \nTarget audience\nOpen to researchers at all career levels as well as research support staff. The workshop language is English. \nIMPORTANT DEADLINES\nDeadline for submission of a proposal for a lightning talk or poster: 8th May\, 2026\nDeadline for event registration: 26th May\, 2026 \nRegistration and learn more: https://danish-repro.github.io/ URL:https://ddsa.dk/event/national-reproducibility-research-data-event-what-matters-to-researchers-and-research-support/ LOCATION:DTU Library\, Anker Engelundsvej 101D\, Kgs. Lyngby\, 2800 CATEGORIES:Other Events ATTACH;FMTTYPE=image/png:https://ddsa.dk/wp-content/uploads/2026/04/repro-event.png ORGANIZER;CN="DTU Library":MAILTO:bibliotek@dtu.dk END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260622T080000 DTEND;TZID=Europe/Copenhagen:20260626T170000 DTSTAMP:20260428T033321 CREATED:20260130T081222Z LAST-MODIFIED:20260130T081222Z UID:10001877-1782115200-1782493200@ddsa.dk SUMMARY:SMARTbiomed summer school in Statistical Genetics\, Causal Inference\, and Machine Learning with Applications to Multi-omics and Clinical Prediction DESCRIPTION:The SMARTbiomed summer school in “Statistical Genetics\, Causal Inference\, and Machine Learning with Applications to Multi-omics and Clinical Prediction” is designed to introduce researchers to modern methods of computational and statistical analysis of complex data. The module content will focus on statistical genetics\, causal inference\, machine learning based approaches. Modules will motivate methods developments through theory in lectures\, but with clear applications in mind\, laid out through extensive\, hands-on practicals throughout the week. The summer school is also a fantastic opportunity to broaden your professional network and to engage with the experts that are part of the SMARTbiomed Pioneer Centre. \nThe SMARTbiomed summer school will run from 22nd-26th June 2026\, at University of Aarhus Sandbjerg residential centre. Further course details at our website: https://smartbiomed.dk/news-and-events/2026-summer-school \nStudents will select one of the following three courses: \n1. Statistical Genetics\nInstructors: Matthew Robinson and Duncan Palmer \nThis course will serve as an introduction to the foundational concepts and methods in statistical genetics\, providing participants the motivation and theory behind key approaches\, as well as practical experience in handling and analysing genetic data. \nTopics will include an exploration of the principles of common and rare variant association testing\, polygenic risk score estimation\, heritability and genetic correlation estimation. \n2. Causal Inference\nInstructors: Erin Gabriel and Michael Sachs \nThis course will introduce foundational concepts in causality and causal inference\, providing participants the background reasoning needed to form estimands and discuss estimators. We will then build on this foundational knowledge\, focusing on methodology through example-driven applications to real-world datasets\, rather than language or concepts. \n3. Machine Learning with Applications to Multi-omics and Clinical Prediction\nInstructors: Adam Hulman and Chris Yau \nThe first part of the course will provide a general introduction to clinical prediction models. Participants will get an overview of the health AI landscape including different data modalities\, and clinically relevant concepts like algorithmic fairness. The second part of the course will focus on multi-omics data modalities; including proteomics\, lipidomics and transcriptomics. We will introduce foundational machine learning concepts\, including supervised and unsupervised learning\, dimensionality reduction\, clustering\, key learning algorithms. We will explore how machine learning can be applied to such multi-omics and multi-modal data\, with an emphasis on advancing our understanding of human diseases and enabling more precise diagnostics\, prevention\, and treatment strategies. URL:https://ddsa.dk/event/smartbiomed-summer-school-in-statistical-genetics-causal-inference-and-machine-learning-with-applications-to-multi-omics-and-clinical-prediction/ LOCATION:University of Aarhus Sandbjerg residential centre\, Sandbjerg Gods\, Sandbjergvej 102\, Sønderborg\, 6400\, Denmark CATEGORIES:PhD Course END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Copenhagen:20260713T080000 DTEND;TZID=Europe/Copenhagen:20260731T170000 DTSTAMP:20260428T033321 CREATED:20260420T072335Z LAST-MODIFIED:20260420T072335Z UID:10001955-1783929600-1785517200@ddsa.dk SUMMARY:Multi-omics approaches for microbial ecology and biotechnology DESCRIPTION:The Microbial Data Science group at the UFZ designed this course focusing on students at different levels of their academic careers (B.Sc.\, M.Sc.\, and Ph.D.) and postdoctoral researchers. When we created this course\, we had two groups in mind. On the one hand\, experienced bioinformaticians would understand how their skills fit microbial ecology and biotechnology research; on the other\, those working in microbial ecology and biotechnology would develop their multi-omics analysis skills. URL:https://ddsa.dk/event/multi-omics-approaches-for-microbial-ecology-and-biotechnology/ LOCATION:Helmholtz Centre for Environmental research – UFZ\, Permoserstraße 15\, Leipzig\, 04318 CATEGORIES:Other Events END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260821 DTEND;VALUE=DATE:20260823 DTSTAMP:20260428T033321 CREATED:20260130T115718Z LAST-MODIFIED:20260130T133706Z UID:10001879-1787270400-1787443199@ddsa.dk SUMMARY:2026 Econometric Models of Climate Change Conference DESCRIPTION:The 10th Conference on Econometric Models of Climate Change (EMCC-X) will be held at Aalborg University\, Denmark\, on August 20-21\, 2026. \nThe conference brings together economists\, econometricians\, climate scientists\, statisticians\, and policy researchers to improve our understanding of climate change\, its impacts\, and effective policy solutions. The EMCC conference series was established in 2016 and has been supported by themed issues of the Journal of Econometrics\, Energy Economics\, the International Journal of Forecasting\, and Advances in Econometrics. URL:https://ddsa.dk/event/2026-econometric-models-of-climate-change-conference/ LOCATION:Aalborg University\, Aalborg\, Fredrik Bajers Vej 7K\, Aalborg\, 9220\, Denmark CATEGORIES:DDSA-Funded Event ATTACH;FMTTYPE=image/jpeg:https://ddsa.dk/wp-content/uploads/2026/01/Aalborg-City.jpg ORGANIZER;CN="Department of Mathematical Sciences%2C Aalborg University":MAILTO:management@math.aau.dk END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260824 DTEND;VALUE=DATE:20260829 DTSTAMP:20260428T033321 CREATED:20260309T121321Z LAST-MODIFIED:20260309T121321Z UID:10001940-1787529600-1787961599@ddsa.dk SUMMARY:PhD Course: Small-angle scattering: Principles\, Data Analysis and Advanced Modeling DESCRIPTION:This course will give a thorough introduction to small-angle x-ray and neutron scattering (SAXS and SANS). The course will cover data collection and basic instrumentation\, but the main focus will be on the analysis of data. This will be facilitated by a range of international lectures\, which are all experts in small-angle scattering. Confirmed guest lecturers: \n– Jan Skov Pedersen\, Department of Chemistry and Interdisciplinary Nanoscience Center (iNANO)\, Aarhus University\, Denmark\n– Wim Bouwmann\, Department of Radiation Science and Technology\, Delft University of Technology (TU Delft)\, Netherlands\n– Michal Hammel\, Berkely Lab\, Molecular Biophysics and Integrated Bioimaging\, Lawrence Berkeley National Laboratory (LBNL)\, California\, USA\n– Annette Langkilde\, Department of Drug Design and Pharmacology\, University of Copenhagen\, Denmark \nThe course has a strong focus on hands-on learning\, to facilitaty the crucial step from understanding to mastering. Therefore\, all learning modules will contain practical aspects or exercises. This is facilitated by our online learning site SASTutorials.org\, which is developed and extended along with the course. The course also includes project work\, where the students work with aspects of their own ph.d. project involving SAXS or SANS. This may include analysis of already collected data\, design of an experiment\, or writing a successfull beamline application. \nOrganizers:\nAndreas H. Larsen\, Niels Bohr Institute\, University of Copenhangen\nJacob J. K. Kirkensgaard\, Niels Bohr Institute and the Department of Food science\, Universtity of Copenhagen URL:https://ddsa.dk/event/phd-course-small-angle-scattering-principles-data-analysis-and-advanced-modeling/ LOCATION:University of Copenhagen\, 4 Frue Plads\, Copenhagen\, 1168\, Denmark CATEGORIES:DDSA-Funded Event,PhD Course ATTACH;FMTTYPE=image/png:https://ddsa.dk/wp-content/uploads/2026/03/front_sponsor.png END:VEVENT END:VCALENDAR