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DTSTART;VALUE=DATE:20260302
DTEND;VALUE=DATE:20260321
DTSTAMP:20260414T211714
CREATED:20260115T114910Z
LAST-MODIFIED:20260205T124805Z
UID:10001867-1772409600-1774051199@ddsa.dk
SUMMARY:Scientific Curation of Natural History Collections
DESCRIPTION:Enrolment guidelines  \nThis is a specialised course where 50% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved for PhD students at other faculties and universities. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe aim is to offer PhD students at the Science Faculty knowledge\, skills and an understanding of the role of scientific curators as well as both practical and theoretical competences for performing scientific curation and managing on natural history collections.\nNatural History collections represent national and international heritage and are actively used for research\, education and communication. In addition to ensuring access to collections and their metadata for international researchers and society\, scientific curators oversee the strategic management and development of the collections. In this course\, we open the treasure chest and introduce you to all the different aspects and possibilities of 21st century natural history collections. \nThe natural history collections of the Natural History Museum Denmark are one of the oldest and largest in the World\, with more than 14 million specimens of plants\, animals\, fossils and rocks\, representing over 400 years of global fieldwork and collection history. The Botanical Garden of the Museum includes over 8.000 living plant species from all over the World as well as a conservation seed bank. \nNatural history collections serve as repositories documenting the distribution of organisms across time and space\, essentially acting as a time capsule. At the same time\, collections\, both living and preserved\, are an immense source of big data for a wide range of research applications from the core discipline of taxonomy to testing evolutionary relationships\, drivers of biodiversity\, and societal challenges including the current climate and biodiversity crisis. \nThe course includes the following themes:\n• General collection management from field expeditions to the collections or garden. From fieldwork planning and ethical considerations\, international collaboration and guidelines to preservation\, identification\, curation\, registration and open access.\n• Strategic development of collections including identification of priority areas for new collecting and exchange with other museums and gardens as well as improving identifications and metadata.\n• Meta-curation of collections across institutions\n• Digitization and imaging of collections\n• Challenges and opportunities of using public databases such as GBIF for research and inventories.\n• Type specimens as the fundamental basis of a species name\, species circumscriptions\, and formal description of a new species.\n• Cultural history of collections.\n• Collection based research across time\, space and species including changes in phenology\, distribution\, invasive species and endangered species.\n• Use of collections to address societal challenges including conservation.\n• Genomic work with historical collections and challenges related to ancient DNA.\n• Communicating collections through exhibitions\, social media\, popular engagement and citizen science.\n• Fund raising for collections and research projects. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\n• Basic understanding of curatorial tasks and considerations related to preserve\, develop\, and communicate natural history collections.\n• Basic understanding of the taxonomic hierarchy\, species concepts\, taxonomical nomenclature\, typification and species description.\n• Understand and describe the cultural\, historical and scientific value of natural history collections and their metadata.\n• Identify and communicate the potential of collections for addressing fundamental science questions and societal challenges to diverse audiences.\n• Assess research and exhibition potentials of the collections.\n• Identify initiatives to make the collections more accessible and of use to the broader public. \nSkills:\n• Responsible and sustainable handling of collections and their metadata.\n• Outline strategy\, development\, and management plans for collections.\n• Describe examples of collection-based research and the use of collections to address societal challenges.\n• Design experiments to investigate collection-based research questions.\n• Communicate the cultural and scientific value of natural history collections. \nCompetences:\n• Assess the values\, strengths\, weaknesses and risks of natural history collections and suggest strategic priorities for collection development.\n• Describe collection management needs in general terms and prepare guidelines for the scientific curation of collections.\n• Extract\, present\, and critically discuss in detail the objectives\, methods and results of scientific articles about collection-based research.\n• Quality assessment of metadata and cleaning of database output for use in research or inventories.\n• Outline future research and prepare funding proposals.\n• Identify communication potential of collections and prepare communication plans and activities.\n• Present their own work (in oral and written form) at a level approaching the scientific standard.\n• Identify interdisciplinary collaboration potentials.\n• Create synergy between collections\, research\, education and public engagement. \nTarget Group\nOur main target group is PhD students at NHMD as well as closest departments at the Science Faculty: IGN and BIO. We expect the course will also attract students from all across Science and we will advertise it internationally as we know there is a huge interest in this course from our international museum and university partners. The course is intended as an obligatory introduction to collections curation for PhD students at NHMD as a basis for the PhD School of Science requirement of duty hours. \nRecommended Academic Qualifications\nBasic knowledge of organismal biology and/or geology\, the taxonomic hierarchy\, species concepts\, phylogeny\, microscopy and DNA work is recommended. \nResearch Area\nCuration\, Taxonomy\, Systematics\, Botany\, Zoology\, Geology\, Palaeontology and Digital Sciences \nTeaching and Learning Methods\nThe course is based on actively working with the museum’s natural history collections and consists of a mixture of lectures\, research presentations from museum staff\, hands-on work in the collections\, project work\, and communication/public engagement activities. During the course students work together on selected projects on a part of the collections. All projects include identifying strategic priority areas\, making action plans for improving the value and use of a part of the collection\, writing a simple grant application and communication plan\, and actively communicating a story or aspect of the collections to a public audience. A labwork component may be included depending on interest of the course participants. \nType of Assessment\nThe students will hand in a 5-page assignment which will be either a collection policy\, strategy or a funding application aimed at attracting external funding for developing a curatorial area. \nLiterature\nWill be provided prior to the course \nCourse coordinator\nProfessor Anders P. Tøttrup\, aptottrup@snm.ku.dk \nGuest Lecturers\nAdd information on the guest lecturers\, including affiliated institution and their contribution to the course \nDates\n2-20 March 2026\nTeaching will occur 2 – 13 March 2026\nHand-in assignment 16 – 20 March 2026 \nExpected frequency\nThe course will run annually. \nCourse location\nThe course will mainly run from the Zoological Museum Building\, Universitetsparken 15\, but teaching will also take place on several other of the museum addresses \nRequirements for signing up\nWith registration we ask for ½-1 page letter stating taxonomic research area and interest.\nPlease send this to: Professor Anders P. Tøttrup\, aptottrup@snm.ku.dk \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 6000 DKK\n• PhD student from foreign university: 6000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 6000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 6000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 16.800 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/__trashed-119/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260202
DTEND;VALUE=DATE:20260207
DTSTAMP:20260414T211714
CREATED:20251112T115259Z
LAST-MODIFIED:20251112T115259Z
UID:10001711-1769990400-1770422399@ddsa.dk
SUMMARY:Large-Scale Data Analysis with R: Transcriptomics and Metabolomics
DESCRIPTION:Enrolment guidelines  \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nMany experimental procedures such as the various “-omics” techniques routinely employed within biotechnology/biological research fields produce vast amounts of data. Therefore\, the amount of available data in many biological disciplines is steadily increasing. Fundamental knowledge and skills of large-scale computing systems and analysis methods is required to make use of this wealth of information. The purpose of this course is to introduce the theory and practice of large-scale data analysis to students\, which will allow them to perform and assess different types of ”-omics”-scale data procedures\, specifically focusing on Transcriptomic data (RNAseq) and Metabolomic data (LC-MS). \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge\n• The general principles of large-scale data analysis\n• Common pitfalls in large-scale data analysis\n• The basic concepts underlying clustering and visualization techniques \nSkills\n• How to efficiently keep\, move\, and analyse large amounts of data\n• How to structure and perform large-scale data analyses in a coding-based software environment\, such as for example R\n• Handling and modifying large datasets\n• Visualization and dissemination of data \nCompetences\n• Analysing different types of large-scale biotechnology data\n• Critically evaluating the quality of different types of biotechnology data\n• Assessing and understanding results of large-scale data analyses \nTarget Group\nAll PhD-students within biology\, biotechnology\, medicine\, pharmaceutical sciences etc. \nRecommended Academic Qualifications\nBasic statistical understanding equivalent to a MSc from SCIENCE; Beginners level experience with R \nResearch Area\nLarge-scale data analysis of “-omics” data in the biological sciences. \nTeaching and Learning Methods\nLectures and computer exercises \nType of Assessment\nCompletion of the course will rely on the production and acceptance of a complete data analysis report in Rmarkdown. \nLiterature\nOriginal literature\, software manuals and tutorials\, and teacher provided compendia \nCourse coordinator\nHenrik de Fine Licht\, Associate Professor\, hhdefinelicht@plen.ku.dk\nMeike Burow\, Professor\, mbu@plen.ku.dk \nDates\n02-06 February 2026 \nCourse location\nPLEN – Frederiksberg Campus \nRequirements for signing up\nThis course is for PhD-students. If you are not a PhD student\, please contact the course organizers before signing up. \nCourse fee\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/large-scale-data-analysis-with-r-transcriptomics-and-metabolomics-2/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260126
DTEND;VALUE=DATE:20260131
DTSTAMP:20260414T211714
CREATED:20240821T085138Z
LAST-MODIFIED:20251112T115123Z
UID:10001346-1769385600-1769817599@ddsa.dk
SUMMARY:Fundamentals of the PhD education at SCIENCE  - module 2 -K8
DESCRIPTION:Enrolment guidelines  \nThe module is mandatory for all new PhD students enrolled at Science from 1 January 2024 as part of the course ‘Fundamentals of the PhD education at SCIENCE’.  \nIf you are a double degree student with a foreign host\, you can not enroll in this course. \nLearning objectives\nThe learning outcomes for the segments are given below. Each outcome is marked K for Knowledge\, S for Skills\, or C for Competences. \nData Management segment:\n• Identify relevant legislation\, requirements\, and policies on data management applicable to research projects at UCPH (K).\n• Recognize recommendations and requirements regarding open and reproducible research designs\, data collection\, and data publication (K).\n• Classify data and conduct a risk assessment to ensure the secure storage of data (S).\n• Assess how data can be preserved and shared to guarantee FAIR use of data (S).\n• Assess when to use electronic lab notes (S).\n• Contribute to planning and conducting appropriate data and materials management in all phases of their PhD project (C).\n• Be able to adhere to best practices of open and reproducible research (C).\nCareer Management segment:\n• Differentiate typical career paths for SCIENCE PhDs (K).\n• Describe selected understandings of motivation (K).\n• Explore and explain their career priorities (S).\n• Build a professional profile on LinkedIn (S).\n• Assess how different uses of their change of scientific environment may impact their career (S).\n• Integrate knowledge of typical career paths for SCIENCE PhDs with an understanding of their personal career priorities (C).\n• Develop a personal networking strategy and build a professional network that supports their career interests (C).\nData Science segment:\n• Know Data Science as a research methodology (K).\n• Apply basic Statistics or Machine Learning computational frameworks and methods when appropriate in their research (S).\n• Build a network for potential inter-disciplinary data science collaborations (C). \nContent\nThe purpose of the course is to introduce the students into Data Science\, Data Management\, and Career Management:\n• Many students will apply/develop Data Science methods (data analysis\, statistics\, and machine learning) directly in their own research; and all students should be aware of this potential.\n• Some students will directly apply Data Management principles\, and all students should be aware of the general policies.\n• All students should actively manage their careers. \nThe module consists of 10 morning/afternoon sessions during the on-campus course week (2 on Data Management\, 1 on Career Management\, and 7 on either Machine Learning or Statistics). Prior to course start\, the students will choose to follow either the Statistics or Machine Learning variant of the module. \nThe aim of the Data Management segment is to equip PhD students with knowledge and skills to:\n• Manage data and primary materials responsibly during their PhD projects.\n• Create open and reproducible research outputs. \nThe aim of the Career Management segment is that PhD students start to explore their values\, motivation\, and the great variety of career options that are open to them after their PhD. The segment will cover:\n• Megatrends in the labour market and typical career paths for PhDs from the natural sciences.\n• Motivation\, values\, and career priorities.\n• Change of scientific environment.\n• Networking for career development. \nThe Data Science segment will aim ensure that PhD students will consider Data Science as a methodology and allow them to apply basic Statistics or Machine Learning methods when appropriate in their research. \nThe Statistics variant will cover:\n• Statistical thinking and methodology\, including statistical power and principles for experimental design.\n• Statistical modelling using fixed and random effects.\n• Tabular and graphical presentation of experimental results and statistical analyses.\n• The R programming environment for Statistics and basic comparisons to other statistical software. \nThe Machine Learning variant will cover:\n• Machine learning foundations and methods.\n• Data Science caveats and best practice.\n• Introduction into AI with focus on intuitive understanding the big models in terms of potential\, applicability\, limitations\, and sustainability.\n• Application of AI and machine learning in science. \nParticipants\nAll PhD students at SCIENCE as part of the PhD Fundamentals course. Module 2 is about 6 months into the PhD programme. \nLanguage\nEnglish \nForm\nThe module will include a mixture of lectures\, exercises\, plenary and group discussions\, and peer feedback during the on-campus segments. Around the on-campus week\, the students will perform eLearning as well as reflective and practical assignments. \n Type of assessment\nAll segments require presence and active participation.\nThe students must hand in a report where they choose between a Data Management Plan\, or a report related to their Statistics or Machine Learning specialization. \nRegistration\nAutomatic registration when enrolling in the PhD program at SCIENCE. \nRemarks\nThe PhD School at the Faculty of SCIENCE is committed to building a learning environment that welcomes\, includes\, and empowers all its PhD students. By building a Faculty-wide peer community of PhD-students with the Fundamentals course\, we secure that all PhD candidates are given adequate instruction in a range of essential competences that lie outside the core scientific research skills offered through supervision\, tool-box and specialized PhD courses. Moreover\, we build bridges between different research programmes at the Faculty of SCIENCE and offer diverse\, multidisciplinary fora of exchange strengthening the PhD candidates’ scientific and social networks and laying the foundation for a strong alumni culture. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/fundamentals-of-the-phd-education-at-science-module-2-k8/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260121
DTEND;VALUE=DATE:20260131
DTSTAMP:20260414T211714
CREATED:20251112T115008Z
LAST-MODIFIED:20251112T115008Z
UID:10001701-1768953600-1769817599@ddsa.dk
SUMMARY:Python for SCIENCE
DESCRIPTION:Enrolment guidelines  \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% og the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nThe course is free of charge for PhD students at Danish universities (except CBS). \nAll other participants must pay the course fee (except if you are a master’s student from a Danish University). \nAim and Content\nThe course introduces to the dominant programming language in data science\, Python. Python is a general-purpose programming language that is currently being used in many active data science projects with open-source libraries available.\nThe workshop will teach the basic programming constructs in Python and then provide data science examples\, including data import\, visualization\, and analysis. We will introduce integrated development interfaces such as JupyterLab. We will introduce libraries from active open-source frameworks (numpy\, pandas\, matplotlib\, sklearn\, …). We will further discuss methods for securing reproducibility of research results (code architecture\, versioning\, open source).\nThe course is aimed at PhD students\, who need tools for data exploration\, data analysis\, and data visualization. Post Docs\, Professors\, and Master’s thesis students from SCIENCE may register for participation and will be accepted if space permits. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge\n• Understand computational thinking concepts.\n• Understand key programming elements (e.g. variables\, objects\, functions\, modules).\n• Know useful open-source libraries (e.g. pandas\, matplotlib\, sklearn). \nSkills\n• Develop/adapt/extend a computer-based software program for analysis of relevant data.\n• Apply good practice co-development principles. \nCompetences\n• Propose relevant analysis methods for scientific data science problems.\n• Consider cross-disciplinary data science methods in their research. \nTarget Group\nPhD students from all SCIENCE departments with an element of data science in their research project. \nRecommended Academic Qualifications\nNone. \nResearch Area\nAll SCIENCE research fields\, and secondarily other scientific fields with a data science element (e.g. health sciences). \nTeaching and Learning Methods\nThe course is composed of sessions combining lectures and exercises.\nFor each topic\, the students will get hands-on experience in applying\, modifying\, and creating elements of analysis methods.\nThe programming examples will be implemented in JupyterLab notebooks and in pure Python source files. \nType of Assessment\nThe students need to be physically present and active during the course. \nLiterature\nCourse lecture slides and exercises.\nWe will use data\, examples\, and other material from publicly available sources. \nCourse coordinator\nErik Dam\, Professor\, erikdam@di.ku.dk \nDates\nWed 21\, Fri 23\, Mon 26\, Wed 28\, Fri 30 January 2026\, from 9 to 16 all days. \nCourse location\nPhysically on campus.\nTypically at Nørre Campus\,\nalternatively at Frederiksberg Campus. \nSeats to PhD students from other Danish universities will be allocated on a first-come\, first-served basis and according to the applicable rules.\nApplications from other participants will be considered after the deadline for registration. \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/python-for-science-2/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251029
DTEND;VALUE=DATE:20251108
DTSTAMP:20260414T211714
CREATED:20250807T124028Z
LAST-MODIFIED:20250807T124028Z
UID:10001522-1761696000-1762559999@ddsa.dk
SUMMARY:Statistical methods for SCIENCE (SmS)
DESCRIPTION:Content \nToolbox course on statistical methodology with focus on choice of statistical models\, practical implementation using statistical software\, and presentation and interpretation of results. For the practical implementation\, we use the state-of-the approach for data analysis in R including data wrangling and visualization via the tidyverse package. The course covers the most common statistical models used in the empirical sciences. Specifically\, the following topics are taught: data types\, data visualization\, table-of-counts\, categorical regression\, linear and multilinear regression\, analysis of variance\, random effects\, hypothesis testing and statistical power\, correction for multiple testing\, estimated marginal means and confidence intervals\, and design of experiments. \nFormal requirements \nThere are no formal requirements. However\, recommended prerequisite is some introductory statistics course during the participant’s bachelor or master studies\, or the PhD school Fundamentals II course. \nLearning outcome \nThe students are introduced to statistical models commonly used in the empirical sciences for univariate end-points. The statistical methodology is discussed with emphasis on how models are used and interpreted\, and the students are trained to do the statistical analyses using the statistical software R. \nAfter course completion\, the students should be able to: \nKnowledge:\n• Understand elements of frequentist statistics including estimation\, confidence intervals\, hypothesis tests\, model validation.\n• Understand data types and organization in tidy data.\n• Understand assumptions for statistical analyses.\n• Understand concepts of fixed and random effects.\n• Understand solutions to the multiple testing problem. \nSkills:\n• Identify the data types in a particular dataset\, and choose an adequate statistical model.\n• Make high quality visualizations of data.\n• Report results via the estimated-marginal-means technology.\n• Use R to perform the statistical analysis via the RStudio interface.\n• Use relevant R packages. In particular\, tidyverse\, emmeans\, and lme4. \nCompetences:\n• Formulate scientific questions in terms of statistical hypothesis.\n• Conduct statistical analysis using the discussed models.\n• Interpret the results of a statistical analysis.\n• Critically reflect over the results\, conclusions and limitations of a statistical analysis.\n• Judge when to seek help from a skilled statistician. \nLiterature \n• Chester Ismay\, Albert Y. Kim: “Statistical Inference via Data Science: A ModernDive into R and the Tidyverse”\, CRC Press\, The R Series\, 2019. Book is also available online at www.moderndive.com.\n• Supplementary material on random effects and estimated marginal means.\n• Software R and RStudio is free and may be downloaded from www.r-project.org and www.posit.co. \nTeaching and learning methods \nLectures and exercises including use of computers. In the first half of the course days focus will be on lectures\, and in the second half on individual coursework with exercises. Participants must bring their own laptops with R and RStudio installed. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/statistical-methods-for-science-sms/
LOCATION:Department of Mathematical Sciences                Universitetsparken 5
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250924
DTEND;VALUE=DATE:20251023
DTSTAMP:20260414T211714
CREATED:20250807T122053Z
LAST-MODIFIED:20250807T122053Z
UID:10001662-1758672000-1761177599@ddsa.dk
SUMMARY:Applied High Performance Computing
DESCRIPTION:Aim and content \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% og the seats are reserved to PhD students from other Danish Universities/faculties (except CBS).\nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list.\nThe course is free of charge for PhD students at Danish universities (except CBS).\nAll other participants must pay the course fee (except if you are a master’s student from a Danish University). \nAim\nComputational methods are becoming essential in many areas of science\, and the solution to many problems depend on computers that are vastly faster and holds more memory than what a single high-end server can offer. Top supercomputers consist of up to a billion processor cores working in parallel and new supercomputers are mostly based on GPUs for high performance modelling and AI workloads. Programming such highly parallel computers is difficult\, and ensuring both program correctness and high performance is non-trivial.\nIn this course students will learn how to get high performance from applications\, how to use accelerators (GPUs)\, and how to parallelise programs inside a single server (shared mememory parallelisation) and across many computers (distrbuted memory parallelisation).\nLectures will introduce the theoretical concepts\, and it is put in to practice through hands-on exercises. Students will learn to map algorithms to parallel architectures and how to decompose problems for parallel execution.\nWe will use ERDA to execute the programs on a real high performance computing infrastructure and evaluate both performance\, scalability and correctness of the programs. The hands-on exercises use real-world examples to illustrate different techniques that are well-suited to each parallel architecture.\nDuring the exercises we will each week introduce a new tool to aid in the development of high performing programs.\nWe will use Python\, C++\, and Fortran as the course languages\, and most exercises will be available in all three\, while some will only be avilable in C++ and Fortran. The students can use the language of their choice to complete the exercises. \nDetailed Content\nWeek 1:\nSingle core performance: Memory access\, vectorization\, Structure-of-Arrays vs Arrays-of-Structures\nTools: Performance profiler\, Makefile\, Debugger\nPlatform: ERDA DAG\nExercise: Molecular dynamics [Python\, C++\, Fortran]\nWeek 2:\nData processing: Task farming\, message passing\nTools: SLURM batch system\nPlatform: ERDA MODI SLURM cluster\nExercise: data processing workflows [Python\, C++\, Fortran]\nWeek 3:\nShared memory architecture: Threads\, OpenMP\nTools: Thread checker\nPlatform: ERDA DAG\nExercise: Propagation of a seismic wave [C++\, Fortran]\nWeek 4:\nGPUs: SIMT architecture\, programming for an infinite number of cores\nTools: GPU profiler\nPlatform: ERDA DAG with datacenter GPUs \nWeek 5:\nDistributed memory architecture: MPI\, domain decomposition\nTools: debugging and benchmarking across many computers\nPlatform: ERDA MODI SLURM cluster\nExercise: Flat world climate model [Python\, C++\, Fortran]\nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\nThe students will understand the challenges in addressing parallelization and adaptation to GPUs of applications and limitations of the available hardware. \nSkills:\n• Design and implement parallel applications.\n• Use a SLURM batch system to execute large parallel applications on a supercomputer.\n• Implement simple task farming to scale data analytics applications across many computers for orders of magnitude reduction in time-to-solution.\n• Adapt a program to execute in parallel on a shared memory computer using OpenMP.\n• Parallelize across computers with Message Passing Interface (MPI).\nTransform a program to execute efficiently on a GPUs and understand when it is beneficial. \nCompetences:\nThe overall purpose of this course is to enable the student to write high performance parallel applications on a range of parallel computer architectures and be able to deploy data analytics workloads effortlessly on a supercomputer using a batch system. \nTarget Group\nThe course is aimed at PhD students\, who need to understand how to use GPUs and parallel computing tools to scale their applications and data analytics workflows. \nRecommended Academic Qualifications\nAcademic qualifications equivalent to a MSc degree.\nIt is necessary to have basic programming experience. Having some experience with applications in scientific modelling\, simulation or data-processing is useful. Course languages are Python\, C++ and Fortran. \nResearch Area\nThis course is broadly relevant for students working with large-scale data analysis and modelling both in SCIENCE and in other fields\, such as life sciences\, social sciences and economics. \nTeaching and Learning Methods\nThe course is composed of sessions combining lectures and exercises. Each week a new topic will be introduced. and the students will get hands-on experience in applying\, modifying\, and programming.\nThe student can choose the programming language that is most relevant for them. \nThe generic structure for each week is:\n• Preparation:\no background literature to give an overview and provide reference material\no motivational video explaining the science behind the exercise\no video introduction to the exercise \n• Lecture:\no Covering the theory behind the topic\, the relation and limitations in terms of hardware\, and an introduction to the relevant methods \n• Class instruction:\no Introduction to exercise\no Introduction to relevant tools\no Hands-on help with exercise \n• Exercise:\nO Practical exercise allowing the student to put the theory in practice using the programming language of choice \nType of Assessment\nTo pass the course\, the student must hand-in at least four of the five exercises. Solutions to all exercises and general feedback on the exercises will be provided afterwards. \nLiterature\nLiterature will be provided on the Absalon page \nCourse coordinator\nTroels Haugbølle\, Asssociate Professor\, haugboel@nbi.ku.dk \nDates\nEvery Wednesday 9 – 17 in a 5 week period starting last week in September 2025.\nWeeks 39\, 40\, 41\, 43\, 44. \nCourse location\nTBD \nRegistration \nDeadline for registration: 01 September 2025 \nSeats to PhD students from other Danish universities will be allocated on a first-come\, first-served basis and according to the applicable rules.\nApplications from other participants will be considered after the deadline for registration. \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant type – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/applied-high-performance-computing/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250915
DTEND;VALUE=DATE:20250920
DTSTAMP:20260414T211714
CREATED:20250312T113154Z
LAST-MODIFIED:20250807T122701Z
UID:10001474-1757894400-1758326399@ddsa.dk
SUMMARY:Sensory Evaluation and Food Preferences - Classic and Digital Approaches - 5 ECTS - 2025
DESCRIPTION:Content \nAim and content \nThe PhD course is a biennial intensive course for PhD students in need of carrying out sensory or consumer acceptance studies as part of their PhD project. The course introduces the foundations of sensory science and practice of performing sensory tests with trained panels and by consumers including their design and execution and assessing sensory and consumer data. \nIn the 2025 edition of the course\, we include the new possibilities of machine learning methods like those based on Natural Language Programming that can be used to enrich sensory and consumer research. Students will have hands-on practice with R programming. Furthermore\, we will introduce how AI tools can be used in processing of sensory and consumer data. \nThe course consists of intensive course days in Copenhagen in addition to a course report on a sensory or consumer food choice behaviour topic related to PhD studies\, including a perspective section on the role of machine learning on the chosen topic. \nCourse content:\nDay 1 (half day): Course introduction\, the senses and principles of sensory testing\nDay 2: Sensory testing in practice: Descriptive analysis and rapid methods with exercises\nDay 3: Consumer preferences and beyond: theory and practical exercises\nDay 4: Introduction to machine learning and applications of machine learning in sensory research\nDay 5: Machine learning in consumer research\, course wrap-up (half day) \nFormal requirements \nRequirements:\n– PhD students in need of carrying out sensory or consumer acceptance studies as part of their PhD project. Other researchers from academia/industry also accepted if space allows.\n– Short motivation letter (1-2 paragraphs) to explain why they are taking the course and what they expect to get out of the course\n• Knowledge of basic statistics\n• Basic knowledge of R (otherwise we will give suggested resources to review beforehand) \nLearning outcome \nKnowledge:\n• Understand principles of sensory measurements and evaluation methods\n• Understand healthy food preferences in different situational contexts\n• Understand common machine learning concepts\, including associated algorithms and programmatic tools \nSkills:\n• Be able to design sensory experiments\, analyse and interpret sensory results\n• Be able to explore\, regress\, and classify quantitative and natural language data \nCompetences:\n• Can critically assess literature in the fields of sensory and consumer research.\n• Can select appropriate machine learning methodologies for solving research problems in sensory and consumer science.\n• Can carry out appropriate sensory and consumer methodologies in own research project. \nLiterature \nReading materials will be selected for the course by the organisers and the guest lecturers. They will be distributed to the students approximately two weeks before the course. \nTarget group \nPhD students working with sensory or consumer data from all areas\, including but not limited to food science\, nutrition\, biology\, agricultural sciences\, psychology\, neuroscience\, marketing \nTeaching and learning methods \nThe course consists of one or two modules:\nI. Intensive course program in Copenhagen\, Denmark including networking\nII. Individual assignment and preparation of a report (deadline 15 November 2025) \nLecturers \n>Professor Karin Wendin\, Kristianstad University\, Sweden\n>Associate Professor Qian Janice Wang\, University of Copenhagen\n>Associate Professor Sébastien Le\, Agrocampus Ouest\, France\n>Dr. Kevin Kantono IFF\, The Netherlands\n>Professor Lisa Methven University of Reading\, UK\n>Professor Wender Bredie\, University of Copenhagen\, \nRemarks \nThe course fee includes course materials\, coffee/tea during breaks\, lunches and networking dinner. Participants should cover their own expenses of travel and accommodation. \nThe course fee will depend on the affiliation of the participants as follows:\n• PhD students: 2\,000 DKK\n• Postdoctoral university staff/Non-profit: 4\,000 DKK\n• Industry/For-profit: 9\,000 DKK\nFull refund if cancellation latest two weeks before course start. \nCo-organisers are Professors Wender Bredie and Karin Wendin \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/sensory-evaluation-and-food-preferences-classic-and-digital-approaches-5-ects-2025/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250825
DTEND;VALUE=DATE:20250830
DTSTAMP:20260414T211714
CREATED:20250505T124927Z
LAST-MODIFIED:20250505T124927Z
UID:10001603-1756080000-1756511999@ddsa.dk
SUMMARY:Methods for Statistical Evaluation of AI
DESCRIPTION:Aim and content \nThe aim of course is to equip the participants with statistical methods\, and knowledge of the newest research within statistical methods\, for evaluation of machine learning and AI. \nThe course runs as a summer school\, retreat for five full days. As preparation\, the students prepare a poster framing their own research in relation to the theme of the summer school. This includes reading selected papers as preparation before the summer school. The posters will be presented by the students at a poster session the first evening of the summer school. The posters are used for group work and further refinement through an assignment\, which finalizes the course. \nThe five course days exists of lectures\, including smaller assignments and discussion to activate the students during lectures. The lecturers consist of both external lecturers as well as lecturers from Danish Universities. Group work is also carried out during the five days of summer school. An individual assignment is handed in one week after the summer school. \nFormal requirements \nThe students need qualifications in statistical methods and machine learning or in one of these areas in depth. \nLearning outcome \nKnowledge:\n• Understand a variety of statistical methods\, including PAC bounds\, measures for evaluation of AI\, and fairness metrics\, fairness calibration and their short comings. \nSkills:\n• Choose between a selection of statistical methods for evaluation of ML and AI\n• Assess fairness of ML models\n• Calibrate for better fairness\, but also understand when calibration can skew fairness even further\n• Apply different sampling strategies for building AI systems\, depending on the requirements of the application. \nCompetences:\n• Develop the competence to critically assess AI models\, identifying strengths\, weaknesses\, and potential biases in model behaviour.\n• Ability to incorporate fairness considerations withing the design and development of ML\, ensuring that ethical principles guide the creation and deployment of AI systems.\n• Build ability to engage in peer review\, provide constructive feedback\, and contribute to the collective advancement of knowledge in statistical AI evaluation.\n• Promote abilities to identify the gaps and open research challenges in statistical evaluation of AI\, including developing methods for necessary and sufficient methods for evaluation of AI models. \nTeaching and learning methods \nLectures\, group work\, and individual assignments. \nLecturers \nExternal guest lecturers: \n• To be confirmed: Gaël Varoquaux\, INRIA\, INRIA Saclay-Ile-de-France Research Centre\, France :\nCourse (tentative title): Evaluation of Machine Learning models on typical tabular data.\nShort bio: Research direction of the Soda team in INRIA. He is specialized in applications of Machine Learning to health. Prof. Varoquaux is a co-founder of the Scikit-learn library that is a widely used Machine Learning library in Python. \n• Benjamin Guedj\, Department of Computer Science\, University College London\, United Kingdom:\nTitle: Artificial intelligence from a statistical perspective.\nShort bio: Dr. Benjamin Guedj is specialized in machine learning. He is a Research Fellow at University College London (UCL) in the Department of Computer Science and a research scientist at Inria\, France. His research focuses on theoretical machine learning\, including statistical learning theory\, PAC-Bayes\, and generalization for deep learning. \n• To be confirmed: Jose Hernandez-Orallo\, Valencian Research Institute for Artificial Intelligence\, Universitat Politècnica de València\, Spain:\nCourse (tentative title): Evaluation of cognitive capabilities of AI systems.\nShort bio: The main research of Prof. Hernández-Orallo focuses on AI evaluation\, benchmarking for measuring AI capabilities\, and the safety of AI systems. \nGuest lecturers from Danish institutions: \n– To be confirmed:  Prof. Aasa Feragen\, Image Analysis and Computer Graphics\, DTU-Compute\, Technical University of Denmark.\nCourse (tentative title): Geometric Statistics in Image Analysis. \n– To be confirmed: Prof. Christian Igel\, Department of Computer Science\, University of Copenhagen\, Denmark.\nCourse (tentative title): PAC-Bayesian Analysis of Ensemble Machine Learning Models. \n– Prof. Line Clemmensen\, Dept. of Mathematical Sciences\, University of Copenhagen and DTU-Compute\, Technical University of Denmark.\nTitle: Data representativity for Machine Learning and AI systems. \n– Prof. Murat Külahci\, Statistics and Data Analysis\, DTU-Compute\, Technical University of Denmark.\nCourse (tentative title): Active learning in time-series and sequential data. \n– Assoc. Prof. Andres Masegosa. Department of Computer Science at Aalborg University\, Technical University of Denmark.\nCourse (tentative title): Generalization of Deep Neural Networks. \nRemarks \nThe participant fee is 5\,600 DKK and covers five days incl overnight stays\, food\, social event\, and all course content. \nIt is co-hosted with D3A. \nHow to apply:\nPhD students enrolled at University of Copenhagen (UCPH)\n1) Please provide us with “stedkode” and “alias” when you register by choosing “Apply” at the upper right corner of the course description\, so that UCPH Accounting Department can make an internal transfer regarding payment of the Participation fee.\nIf you have been accepted for the course\, we will verify your enrollment via mail. \nPhD students enrolled at other universities than UCPH\n1) Choose “Apply” in the upper right corner of the course description.\nIf you have been accepted for the course\, we will send you a mail with a link to payment of the participant fee.\nNB: Payment via creditcard or Mobilepay only. After you have made your payment of the participation fee\, we will verify your enrollment via mail. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/methods-for-statistical-evaluation-of-ai-2/
CATEGORIES:DDSA-Funded Event,PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250811
DTEND;VALUE=DATE:20250816
DTSTAMP:20260414T211714
CREATED:20240821T085338Z
LAST-MODIFIED:20250310T104640Z
UID:10001338-1754870400-1755302399@ddsa.dk
SUMMARY:Quantitative Sustainability Assessment
DESCRIPTION:Aim and content \nThe course is designed to equip students with the necessary tools to reflect upon and quantify sustainability performance\, a crucial component in research\, policymaking\, corporate decision making\, and reporting. It focuses on introducing the fundamental principles of quantitative sustainability assessment of various production systems\, including dynamic carbon modelling and utilizing unexplored side streams along value chains. \nKey methodological approaches such as material flow analysis\, input-output modelling\, life cycle assessment\, and carbon dynamics will be covered\, with an emphasis on defining functional units\, time scales\, and system boundaries. Participants will have the freedom to select from these methods to apply to their own data sets\, which they are encouraged to bring. This practical application will be facilitated through individual or group problem-oriented projects\, employing a variety of analytical tools and methods chosen by the students. \nThroughout the course\, critical topics such as resource and emission capture\, zero waste strategies\, and cascade utilization within circular business ecosystems will be explored through interactive lectures and practical applications. This approach fosters an environment of active learning\, allowing participants to directly engage with the material and apply what they learn to real-world scenarios. \nFormal requirements \nParticipants should have a Master’s degree or equivalent. Proficiency in data analysis with tools such as R\, Python\, MATLAB\, SAS\, or Excel is beneficial. A foundational understanding of life cycle thinking or knowledge of LCA is crucial. This background will enable effective engagement with the course’s focus on sustainable resource management and environmental evaluations. \nLearning outcome \nStudents who have completed the course will be able to: \nKnowledge:\n \n\nUnderstand the principles\, requirements\, and limitations behind quantitative sustainability assessment methods such as material flow analysis\, life cycle assessment (LCA)\, input-output modeling\, and dynamic carbon modeling.\nGrasp the importance of selecting meaningful parameters when defining system boundaries\, functional units\, and allocation methods.\nRecognize the role of unexplored sidestreams in enhancing sustainability within circular business ecosystems.\n\n\nSkills: \n\nApply various quantitative sustainability assessment tools and methods to their own data sets\, enhancing their ability to analyze and predict environmental impacts.\nSelect appropriate tools and methodologies freely\, including selected software or Excel\, to address specific research questions in process optimisation\, business or policy implications.\nBased on the aim of the assessment\, discuss\, identify and rank which factors should be included in a sustainability assessment.\nBe able to engage in the public debate about the sustainability of transitions.\n\n\nCompetences: \n\nDescribe and discuss quantitative sustainability assessment methods.\nBe able to reflect on how findings of a given sustainability assessment is influenced by the choice of critical parameters such as functional unit\, allocation methods\, and system boundaries.\nCritically assess and enhance the sustainability of different bioeconomic processes through evaluation techniques.\nEngage effectively in interdisciplinary settings\, utilizing their knowledge to contribute to sustainable development goals and circular economy initiatives.\nImplement resource management and systems design strategies to optimize sustainability and efficiency in various bioeconomic contexts.\n\nTarget group \nThis course is designed for PhD students engaged in various sectors of the bioeconomy\, particularly those from forestry\, food science\, agriculture\, biotechnology\, and environmental science. It is ideal for individuals interested in sustainable innovation in resource management and technologies within circular economy implementations across these critical areas. Emphasizing resource and emission capture and utilization\, zero waste practices\, and cascade utilization strategies\, the course prepares students to develop and implement circular business models that optimize resource use and minimize environmental impact.  \nThe course aims to attract a diverse group of students\, including those in continuing education programs\, who are eager to apply circular economy principles to enhance sustainability in the forest and food sectors\, as well as other related bioeconomic fields. This interdisciplinary approach ensures that participants from different backgrounds can contribute to and benefit from the course\, fostering a comprehensive understanding of circular bioeconomy and promoting practices that lead to more sustainable and resilient bioeconomic sectors. \nTeaching and learning methods \nThe course is conducted through various teaching and learning methods: \nLectures: \nTo present the fundamentals and applications of quantitative sustainability methods\, such as dynamic carbon modeling and life cycle assessment\, within the context of circular economy principles. \nExercises: \nTo train students in applying these methods using a variety of tools\, enhancing their practical skills in sustainability analysis. \nCase Work: \nStudents are encouraged to bring their own data sets for problem-oriented projects\, applying the learned concepts to real-world scenarios either individually or in collaborative groups.\n \nDiscussions and Reflections: \nRegular sessions are held to discuss the opportunities and limitations of sustainability assessment\, fostering a critical understanding of resource management and systems design within various bioeconomic sectors. \nAssessment: \nCourse participation is assessed based on an individually prepared essay/report outlining the role of sustainability assessment in their own PhD project and how it could be assessed\, including pros and cons of the chosen method(s). \nLecturers \nCourse organisers: \n\nProfessor Lisbeth G. Thygesen\, Department for Geosciences and Natural Resource Management\nProfessor Marianne Thomsen\, Department of Food Science\nAssociate Professor Niclas Scott Bentsen\, Department for Geosciences and Natural Resource Management\n\nThe course will include guest lecture/-s from academia and/or industry\, will share practical insights into sustainability and circular bioeconomy\, enhancing the applicability of course concepts in real-world settings. \nRemarks \nNo course fee. The course will be cancelled if less than 10 students sign up for it.\n \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/quantitative-sustainability-assessment/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250630
DTEND;VALUE=DATE:20250705
DTSTAMP:20260414T211714
CREATED:20250310T104250Z
LAST-MODIFIED:20250310T104250Z
UID:10001467-1751241600-1751673599@ddsa.dk
SUMMARY:Introduction to Nutritional Metabolomics
DESCRIPTION:Aim and content \nThe aim of this course is to introduce the students to all phases in a nutritional metabolomics study\, to instruct the students on sample handling\, and to train the student in data analysis and in the use of freely available tools for the metabolomics data flow. \nThe course will provide a general overview of LC-MS based untargeted metabolomics from study design to results and will be exemplified with its specific application in nutrition. It will be delivered using a mixture of lectures\, hands-on data preparation and analysis\, computer-based practical sessions\, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but there is no practical lab work. \nThe students will go through common steps in a typical metabolomics study using a real-life case. This case study includes plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF has been conducted and the students will further process and analyze the acquired data with various freeware tools (e.g. R\, XCMS\, MZmine etc). They will finally work on identification of relevant metabolites using manual analysis assisted by several web-based databases and structure elucidation tools. The course will be concluded by presentations of reports generated by the students based on the case study. \nThe students should expect a fairly technical course with a strong focus on the hands-on data analysis abilities and data interpretation skills. Programming skills are not a prerequisite for entering the course and students are guided through the exercises. However\, for students that are not familiar with R we expect them to explore the self-study curriculum based on short videos and texts that cover essential programming concepts.\nThe project work has a high workload\, and hence evening work can be expected during the course week. \nLearning outcome \nKnowledge:\n• Analyze different types of study designs commonly used in metabolomics (e.g.\, interventional study\, case-control\, cohort\, cross-sectional) and evaluate their strengths and weaknesses in terms of validity\, bias\, and applicability.\n• Explain each step of the metabolomics pipeline\, including sample collection\, data acquisition\, preprocessing\, statistical analysis\, and interpretation\, and identify appropriate tools and methods to perform each step effectively. \nSkills:\n• Carry out data preprocessing using freely available tools (R/XCMS)\n• Perform basic univariate and multivariate analysis (e.g. R)\n• Interpret the MS/MS spectra by manual interpretation and by utilizing available tools (e.g. MetFrag\, SIRIUS) and databases (e.g. HMDB\, METLIN and MassBank) \nCompetences:\n• Describe the handling of urine\, plasma and other samples collected from humans for metabolomics analysis\n• Understand the basic principles of UPLC-QTOF technology\n• Suggest which sample type to analyze for a specific research question and propose the relevant sample collection and preparation procedure \nTeaching and learning methods \nBasic principles and an overview are given by mostly frontal lectures that include small tasks and quizzes.\nEach practical step is then explained\, and the students work in groups to perform the tasks on the dataset provided.\nThe course culminates in a presentation where the students describe the steps they took and the conclusions they reached. \nLecturers \nAssistant Professor Jan Stanstrup\, NEXS\nAssociate Professor Henrik Munch Roager\, NEXS\nAssociate Professor Giorgia La Barbera\, NEXS\nAssociate Professor Carl Brunius\, Chalmers University of Technology\, Sweden\nPhD student Francesca Bucci\, NEXS \nRemarks \nBreakfast (light)\, snacks\, lunch and dinner will be provided during the course. \nFee\nThere is no fee for the PhD students under the Open Market in Denmark.\nOther participants are to pay a course fee of 700 EUR.\nYour registration is considered binding and the following rules apply:\n3 weeks before the start of the course\, it is possible to opt out\, without having to pay participation fees. If you opt out beyond this date or do not show up on the course you will be charged the full participation fee\, unless another participant are able to sign up for the course instead.\nThe fee must be paid no later than the 6th of June 2025.\nEach student must pay and arrange their own travel and accommodation in Copenhagen during the course. \nThe preliminary program can be downloaded here. \nFor more information please contact Jan Stanstrup – jst@nexs.ku.dk \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/introduction-to-nutritional-metabolomics/
LOCATION:Department of Nutrition Exercise and Sports
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250521
DTEND;VALUE=DATE:20250523
DTSTAMP:20260414T211714
CREATED:20250305T144330Z
LAST-MODIFIED:20250305T144330Z
UID:10001464-1747785600-1747958399@ddsa.dk
SUMMARY:International School of Chemometrics 2025 - Metabolomics
DESCRIPTION:Aim and content \nFour week school designed to introduce different key aspects of Data Science and Machine Learning in different branches of science (chemistry\, food & feed\, physics\, environmental\, political economics\, etc). The course has the following modules: \n1) PROGRAMMING – Introduction to Programming for Multivariate data analysis in Matlab\, Python and R\nThis online seminar is based on online pre-recorded videos that are thought to be an introduction to the main aspects of dealing with Matlab\, R and Python focused on Multivariate Data Analysis. \n2) BASIC – Basic Introduction to Chemometrics and Linear Algebra\nThis seminar includes two parts:\nEXPLORE (PCA) and LINAL (Linear Algebra). \n3) INTERMEDIATE:\nThis seminar includes three parts:\n• DoE\, Design of Experiments\n• VARSEL\, variable selection methods;\n• MCR\, Multivariate Curve Resolution. \n4) DL – Non-Linear Modeling / Deep Learning\nThis seminar includes two parts: methods for non-linear modeling and the different architectures of Artificial Neural Networks (basic structures\, shallow neural networks and deep neural networks). \n5) Classification\nThe course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. It will also deal with Support Vectors Machine and Random Forests. \n6) Optimization\nThis seminar will give an overview of different optimization methods that are extremely useful for optimizing hyperparameters in models. Methods like Particle swarm optimization (PSO) or Gauss-Newton will be taught and different examples discussed. \n7) Metabolomics\nThis seminar will deal with the chemometric approaches for integrating (“fusing”) data from different sources. First of all\, the various configurations which may occur when dealing with multiple data matrices will be presented and discussed\, and a hierarchy/systematization of the possible data fusion approaches will be introduced. The main multi-block strategies for data exploration and predictive modeling will then be discussed and compared. Further classification of models depending on whether the globally common\, locally common and distinct information is considered or not will also be introduced. The theoretical and algorithmic description of the methods will be accompanied by worked examples of real data sets. \n8) GLUE (How not to make Chemometrics) and WORKSHOP\nWe will take a very close look at all the most common mistakes that even experienced people will do when doing multivariate analysis. We will cover exploration\, calibration\, interpretation\, visualization and many other subjects. This is done with a focus on the most common problems as well as sound alternatives to address them. \nFormal requirements \nNone required. We start from the basics and go all the way to advanced. \nLearning outcome \nKnowledge\nUpon completing the course\, students will:\n– Understand the foundational principles of data science methods\, specifically in chemometrics and multivariate analysis.\n– Gain theoretical knowledge in statistical and machine learning techniques such as PCA\, multivariate regression\, variable selection methods\, and non-linear modeling.\n– Comprehend advanced data analysis methods including multiway data analysis\, ANOVA Simultaneous Component Analysis (ASCA)\, and data fusion strategies.\n– Learn the main experimental designs used in Design of Experiments (DoE)\, their applications\, and their limitations.\n– Understand the implications of improper analysis in chemometrics and methods to avoid common mistakes in multivariate data analysis. \nSkills\nStudents will develop the ability to:\n– Apply data analysis techniques (PCA\, MCR\, etc.) to real-world data sets in their own research fields.\n– Code basic algorithms in Matlab\, Python\, or R for multivariate data analysis and create analytical data pipelines.\n– Perform experimental design using DoE principles to optimize processes and interpret data effectively.\n– Integrate data from multiple sources (Data Fusion) and interpret the resulting fused datasets to address complex research questions.\n– Conduct a critical analysis of data\, identifying and troubleshooting potential errors in data interpretation and visualization. \nCompetences\nBy the end of the course\, students will be able to:\n– Analyze and interpret diverse data types independently\, drawing meaningful conclusions from complex datasets.\n– Solve domain-specific data problems in a structured and reproducible manner.\n– Collaborate effectively with researchers from diverse scientific backgrounds\, communicating data science concepts clearly.\n– Assess and select appropriate data analysis methods based on the research context and the nature of the data. \nTarget group \nAll PhD students who aim to use data science within chemical and related areas. \nTeaching and learning methods \nThe seminars of the School of Chemometrics will consist of a mix of presentations from world leading researchers mixed with practical exercises in data analytic software that provides the student with practical experience on how to apply the tools learned in the course. The exercises are done under the supervision of the teachers. \nThe initial week on programming offers teaching in three different languages and all the teaching in this part is based on e-learning. The student can choose between either programming in MATLAB\, Python or R in this first week. \nThe rest of the school (three weeks) is physical on-site training. \nLecturers \n• Assoc. Prof. Davide Ballabio\, University of Milano-Bicocca will teach several days on classification and general data science\n• Assoc. Prof. Agnieszka Smolinska\, Maastricht University will teach design of experiment\n• Prof. José Amigo Rubio\, University of Basque Country will teach courses on programming\, basic chemometrics\, MCR and general data science. They are also the main responsible for the day-to-day activities throughout the course.\n• There are several other guest lectures but these are the ones for which we have applied for funding \nRemarks \nCourse fee: 6000 DKK for the full course. Fee covers additional invited teachers for the course\, social events\, facilities for poster. \nPhD students enrolled at a Danish PhD school that is a member of the open market for PhD courses: free of charge. \nMaster’s students from Danish universities: free of charge. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2025-metabolomics/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250521
DTEND;VALUE=DATE:20250523
DTSTAMP:20260414T211714
CREATED:20250305T144050Z
LAST-MODIFIED:20250305T144300Z
UID:10001463-1747785600-1747958399@ddsa.dk
SUMMARY:International School of Chemometrics 2025 - DL -Non-Linear Modeling/Deep Learning
DESCRIPTION:Aim and content \nFour week school designed to introduce different key aspects of Data Science and Machine Learning in different branches of science (chemistry\, food & feed\, physics\, environmental\, political economics\, etc). The course has the following modules: \n1) PROGRAMMING – Introduction to Programming for Multivariate data analysis in Matlab\, Python and R\nThis online seminar is based on online pre-recorded videos that are thought to be an introduction to the main aspects of dealing with Matlab\, R and Python focused on Multivariate Data Analysis. \n2) BASIC – Basic Introduction to Chemometrics and Linear Algebra\nThis seminar includes two parts:\nEXPLORE (PCA) and LINAL (Linear Algebra). \n3) INTERMEDIATE:\nThis seminar includes three parts:\n• DoE\, Design of Experiments\n• VARSEL\, variable selection methods;\n• MCR\, Multivariate Curve Resolution. \n4) DL – Non-Linear Modeling / Deep Learning\nThis seminar includes two parts: methods for non-linear modeling and the different architectures of Artificial Neural Networks (basic structures\, shallow neural networks and deep neural networks). \n5) Classification\nThe course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. It will also deal with Support Vectors Machine and Random Forests. \n6) Optimization\nThis seminar will give an overview of different optimization methods that are extremely useful for optimizing hyperparameters in models. Methods like Particle swarm optimization (PSO) or Gauss-Newton will be taught and different examples discussed. \n7) Metabolomics\nThis seminar will deal with the chemometric approaches for integrating (“fusing”) data from different sources. First of all\, the various configurations which may occur when dealing with multiple data matrices will be presented and discussed\, and a hierarchy/systematization of the possible data fusion approaches will be introduced. The main multi-block strategies for data exploration and predictive modeling will then be discussed and compared. Further classification of models depending on whether the globally common\, locally common and distinct information is considered or not will also be introduced. The theoretical and algorithmic description of the methods will be accompanied by worked examples of real data sets. \n8) GLUE (How not to make Chemometrics) and WORKSHOP\nWe will take a very close look at all the most common mistakes that even experienced people will do when doing multivariate analysis. We will cover exploration\, calibration\, interpretation\, visualization and many other subjects. This is done with a focus on the most common problems as well as sound alternatives to address them. \nFormal requirements \nNone required. We start from the basics and go all the way to advanced. \nLearning outcome \nKnowledge\nUpon completing the course\, students will:\n– Understand the foundational principles of data science methods\, specifically in chemometrics and multivariate analysis.\n– Gain theoretical knowledge in statistical and machine learning techniques such as PCA\, multivariate regression\, variable selection methods\, and non-linear modeling.\n– Comprehend advanced data analysis methods including multiway data analysis\, ANOVA Simultaneous Component Analysis (ASCA)\, and data fusion strategies.\n– Learn the main experimental designs used in Design of Experiments (DoE)\, their applications\, and their limitations.\n– Understand the implications of improper analysis in chemometrics and methods to avoid common mistakes in multivariate data analysis. \nSkills\nStudents will develop the ability to:\n– Apply data analysis techniques (PCA\, MCR\, etc.) to real-world data sets in their own research fields.\n– Code basic algorithms in Matlab\, Python\, or R for multivariate data analysis and create analytical data pipelines.\n– Perform experimental design using DoE principles to optimize processes and interpret data effectively.\n– Integrate data from multiple sources (Data Fusion) and interpret the resulting fused datasets to address complex research questions.\n– Conduct a critical analysis of data\, identifying and troubleshooting potential errors in data interpretation and visualization. \nCompetences\nBy the end of the course\, students will be able to:\n– Analyze and interpret diverse data types independently\, drawing meaningful conclusions from complex datasets.\n– Solve domain-specific data problems in a structured and reproducible manner.\n– Collaborate effectively with researchers from diverse scientific backgrounds\, communicating data science concepts clearly.\n– Assess and select appropriate data analysis methods based on the research context and the nature of the data. \nTarget group \nAll PhD students who aim to use data science within chemical and related areas. \nTeaching and learning methods \nThe seminars of the School of Chemometrics will consist of a mix of presentations from world leading researchers mixed with practical exercises in data analytic software that provides the student with practical experience on how to apply the tools learned in the course. The exercises are done under the supervision of the teachers. \nThe initial week on programming offers teaching in three different languages and all the teaching in this part is based on e-learning. The student can choose between either programming in MATLAB\, Python or R in this first week. \nThe rest of the school (three weeks) is physical on-site training. \nLecturers \n• Assoc. Prof. Davide Ballabio\, University of Milano-Bicocca will teach several days on classification and general data science\n• Assoc. Prof. Agnieszka Smolinska\, Maastricht University will teach design of experiment\n• Prof. José Amigo Rubio\, University of Basque Country will teach courses on programming\, basic chemometrics\, MCR and general data science. They are also the main responsible for the day-to-day activities throughout the course.\n• There are several other guest lectures but these are the ones for which we have applied for funding \nRemarks \nCourse fee: 6000 DKK for the full course. Fee covers additional invited teachers for the course\, social events\, facilities for poster. \nPhD students enrolled at a Danish PhD school that is a member of the open market for PhD courses: free of charge. \nMaster’s students from Danish universities: free of charge. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2025-dl-non-linear-modeling-deep-learning-1-5-ects/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250519
DTEND;VALUE=DATE:20250524
DTSTAMP:20260414T211714
CREATED:20250423T111247Z
LAST-MODIFIED:20250423T111247Z
UID:10001604-1747612800-1748044799@ddsa.dk
SUMMARY:PhD Toolbox Course: Estimating Causal Effects with Observational Data
DESCRIPTION:Aim and content \nResearchers are usually interested in investigating causal relationships\, i.e.\, how one thing affects another thing. While the analysis of causal relationships is easiest when using experimental data\, in several research areas (e.g.\, social sciences) experiments are not always feasible\, and when they are feasible\, they may suffer from important limitations. As a result\, most empirical studies in the social sciences and in related research areas are based on observational (i.e.\, nonexperimental) data. \nParticipants in this course will learn state-of-the-art empirical methods used for investigating causal relationships with observational data. Course participants will also learn how to evaluate and discuss the appropriateness of research designs and empirical methods (“identification strategies”) for analysing causal relationships\, and they will learn to choose the most appropriate research designs and empirical methods for analysing a specific research question. All this will help participants obtain more credible and reliable results in their empirical work and to publish their work in better journals. \nThe methods that will be taught in this course include\, e.g.\, directed acyclic graphs\, methods based on instrumental variables\, synthetic control methods\, regression discontinuity design\, difference-in-differences\, methods for panel data with staggered treatment\, causal machine learning methods\, etc. The course participants will learn the theoretical background and underlying assumptions of these methods as well as to apply them in\nreal-world empirical analyses. \nCourse venue\nMonday 19-05-2025.room – A2-70.02\, Thorvaldsensvej 40\, Frederiksberg\nTuesday 20-05-2025. room – A1-01.13\, Bülowsvej 17\, Frederiksberg\nWedensday 21-05-2025. room – A2-70.02\, Thorvaldsensvej 40\, Frederiksberg\nThursday 22-05-2025.room – A2-73.01\, Thorvaldsensvej 40\, Frederiksberg\nFriday 23-05-2025. room – A2-84.-11\, Thorvaldsensvej 40\, Frederiksberg \nLearning outcome \nKnowledge:\n• Describe various methods for analysing causal research questions with observational data.\n• For various methods for analysing causal research questions with observational data\, describe the assumptions that need to be fulfilled if the respective method should give reliable estimates of the causal effect. \nSkills:\n• Apply various methods for analysing causal research questions with observational data using (statistical) software such as R\, Stata\, or Python.\n• Assess to which extent assumptions that are required by various methods for analysing causal research questions with observational data are fulfilled in specific real-world applications. \nCompetences:\n• Choose research designs and methods (“identification strategies”) that are appropriate for analysing various causal research questions with observational data in their research area.\n• Critically evaluate the appropriateness of research designs and methods (“identification strategies”) for analysing various causal research questions with observational data in their research area (this refers to their own research\, e.g.\, when discussing strength and weaknesses of their empirical analyses in their own papers\, as well as to the research done by others\, e.g.\, when reviewing manuscripts or assessing the reliability of research done by others for other reasons). \nLiterature \nThe participants will be informed about the course literature at least four weeks before the course starts. The course literature could be\, e.g.\,\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• Henningsen\, A.\, Low\, G.\, Wuepper\, D.\, Dalhaus\, T.\, Storm\, H.\, Belay\, D. and Hirsch\, S (2024): Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists. IFRO Working Paper 2024/03\, Department of Food and Resource Economics\, University of Copenhagen. https://EconPapers.repec.org/RePEc:foi:wpaper:2024_03\n• Morgan\, S.L. and Winship\, C. (2014)\, Counterfactuals and Causal Inference: Methods and Principles for Social Research\, 2nd ed. Cambridge University Press.\n• Journal articles. \nTeaching and learning methods \nThe course participants should read the course material before the course starts to be well prepared for the course. The course consists of lectures\, in which various methods for analysing causal research questions with observational data as well as their underlying assumptions are presented and explained. The participants of the course will also do practical exercises\, in which they learn to implement these methods in practice. While the teachers will use the R software to present solutions to these exercises\, the participants are free to use other software packages (e.g.\, Stata\, Python\, …). The practical exercises also include group discussions\, e.g.\, about the appropriateness of research designs and empirical methods (“identification strategies”). The course participants can choose to write a short report\, in which they apply at least one of the methods taught in the course to real- world observational data\, e.g.\, a part of the analyses that they do in their PhD project. Reproducibility of the empirical analysis will play a key role in\nthe lectures\, the practical exercises\, and in the ‘short report’ (exam). \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 (e.g.\, so that decent journals in the respective research area\nwould assess the quality of the empirical analysis to be appropriate). \nRemarks \nFor all participants there is a participant fee of 1000 DKK that covers coffee\, tea\, and lunch all days \nCours fee: \n– No course fee for PhD students enrolled at SCIENCE\n– No course fee for PhD students enrolled at Danish PhD schools that are members of the open market for PhD courses\n-1200 DKK – PRICING PER ECTS PER PARTICIPANT: PhD students enrolled at Danish PhD schools that are not members of the open market for PhD courses (CBS and Graduate School of Business and Social Sciences AU)\n– 1200 DKK – PRICING PER ECTS PER PARTICIPANT PhD students enrolled at foreign universities \nSome participants have to additional pay a course fee\, see: https://science.ku.dk/phd/courses/databases/Pricing_PhD_courses_at_SCIENCE_2024.pdf \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/phd-toolbox-course-estimating-causal-effects-with-observational-data-2/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250519
DTEND;VALUE=DATE:20250524
DTSTAMP:20260414T211714
CREATED:20250305T143540Z
LAST-MODIFIED:20250305T143658Z
UID:10001505-1747612800-1748044799@ddsa.dk
SUMMARY:Estimating Causal Effects with Observational Data
DESCRIPTION:Aim and content \nResearchers are usually interested in investigating causal relationships\, i.e.\, how one thing affects another thing. While the analysis of causal relationships is easiest when using experimental data\, in several research areas (e.g.\, social sciences) experiments are not always feasible\, and when they are feasible\, they may suffer from important limitations. As a result\, most empirical studies in the social sciences and in related research areas are based on observational (i.e.\, nonexperimental) data. \nParticipants in this course will learn state-of-the-art empirical methods used for investigating causal relationships with observational data. Course participants will also learn how to evaluate and discuss the appropriateness of research designs and empirical methods (“identification strategies”) for analysing causal relationships\, and they will learn to choose the most appropriate research designs and empirical methods for analysing a specific research question. All this will help participants obtain more credible and reliable results in their empirical work and to publish their work in better journals. \nThe methods that will be taught in this course include\, e.g.\, directed acyclic graphs\, methods based on instrumental variables\, synthetic control methods\, regression discontinuity design\, difference-in-differences\, methods for panel data with staggered treatment\, causal machine learning methods\, etc. The course participants will learn the theoretical background and underlying assumptions of these methods as well as to apply them in\nreal-world empirical analyses. \nLearning outcome \nKnowledge:\n• Describe various methods for analysing causal research questions with observational data.\n• For various methods for analysing causal research questions with observational data\, describe the assumptions that need to be fulfilled if the respective method should give reliable estimates of the causal effect. \nSkills:\n• Apply various methods for analysing causal research questions with observational data using (statistical) software such as R\, Stata\, or Python.\n• Assess to which extent assumptions that are required by various methods for analysing causal research questions with observational data are fulfilled in specific real-world applications. \nCompetences:\n• Choose research designs and methods (“identification strategies”) that are appropriate for analysing various causal research questions with observational data in their research area.\n• Critically evaluate the appropriateness of research designs and methods (“identification strategies”) for analysing various causal research questions with observational data in their research area (this refers to their own research\, e.g.\, when discussing strength and weaknesses of their empirical analyses in their own papers\, as well as to the research done by others\, e.g.\, when reviewing manuscripts or assessing the reliability of research done by others for other reasons). \nLiterature \nThe participants will be informed about the course literature at least four weeks before the course starts. The course literature could be\, e.g.\,\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• Henningsen\, A.\, Low\, G.\, Wuepper\, D.\, Dalhaus\, T.\, Storm\, H.\, Belay\, D. and Hirsch\, S (2024): Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists. IFRO Working Paper 2024/03\, Department of Food and Resource Economics\, University of Copenhagen. https://EconPapers.repec.org/RePEc:foi:wpaper:2024_03\n• Morgan\, S.L. and Winship\, C. (2014)\, Counterfactuals and Causal Inference: Methods and Principles for Social Research\, 2nd ed. Cambridge University Press.\n• Journal articles. \nTeaching and learning methods \nThe course participants should read the course material before the course starts to be well prepared for the course. The course consists of lectures\, in which various methods for analysing causal research questions with observational data as well as their underlying assumptions are presented and explained. The participants of the course will also do practical exercises\, in which they learn to implement these methods in practice. While the teachers will use the R software to present solutions to these exercises\, the participants are free to use other software packages (e.g.\, Stata\, Python\, …). The practical exercises also include group discussions\, e.g.\, about the appropriateness of research designs and empirical methods (“identification strategies”). The course participants can choose to write a short report\, in which they apply at least one of the methods taught in the course to real- world observational data\, e.g.\, a part of the analyses that they do in their PhD project. Reproducibility of the empirical analysis will play a key role in\nthe lectures\, the practical exercises\, and in the ‘short report’ (exam). \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 (e.g.\, so that decent journals in the respective research area\nwould assess the quality of the empirical analysis to be appropriate). \nRemarks \nFor all participants there is a participant fee of 1000 DKK that covers coffee\, tea\, and lunch all days \nCours fee: \n– No corse fee for PhD students enrolled at SCIENCE\n– No course fee for PhD students enrolled at Danish PhD schools that are members of the open market for PhD courses\n-1200 DKK – PRICING PER ECTS PER PARTICIPANT: PhD students enrolled at Danish PhD schools that are not members of the open market for PhD courses (CBS and Graduate School of Business and Social Sciences AU)\n– 1200 DKK – PRICING PER ECTS PER PARTICIPANT PhD students enrolled at foreign universities \nSome participants have to additional pay a course fee\, see: https://science.ku.dk/phd/courses/databases/Pricing_PhD_courses_at_SCIENCE_2024.pdf \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/phd-toolbox-course-estimating-causal-effects-with-observational-data/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250429
DTEND;VALUE=DATE:20250528
DTSTAMP:20260414T211714
CREATED:20250305T141150Z
LAST-MODIFIED:20250305T141150Z
UID:10001526-1745884800-1748390399@ddsa.dk
SUMMARY:Machine Learning for SCIENCE (MLS)
DESCRIPTION:Aim and content \nThe course will take place over five consecutive Tuesdays\, starting Apr. 29\, 2025 (i.e. April 29. May 6\, May 13\, May 20\, May 27). \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 outcomes. 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 synopsis with a suggestion for an analysis ideally performed on their own data including a small implementation of a key concept. This synopsis could form the basis for the Data Science Projects PhD course also offered by the Data Science Lab. \nFormal requirements \nThe number of participants is limited at 50\, and priority will be given to PhD students enrolled at UCPH-SCIENCE and participants from a previous Data Science Lab course (Introduction to Python or R\, Statistical Methods I). \nWe assume that the students have some experience with Python programming. \nLearning outcome \nAfter course completion the students are expected to be able to: \nKnowledge:\n– Understand key machine learning concepts (parameter training\, overfitting).\n– Understand key machine learning methods (LDA\, (un-) supervised learning).\n– Understand key image 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. \nLiterature \nCourse lecture slides and exercises.\nWe will use data\, examples\, and other material from publicly available sources. \nTeaching and learning methods \nThe course is composed of sessions combining lectures and exercises. For 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. \nRemarks \nQuestions:\nIf you have ay questions please contact course organizer Raghavendra Selvan (raghav@di.ku.dk) \nExamination:\nThe students need to hand in their synopsis (10 days after the final course day). The synopsis must be approved. The students are allowed to work in 2-person groups. \nParticipation fee:\nPhD students enrolled at the PhD School of SCIENCE are exempt from the participation fee.\nAll other students are required to pay the participation fee of DKK 3600. \nDetails and Updates:\nFor details for this and other Data Science Lab courses\, see: http://datalab.science.ku.dk/english/course/ \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/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250428
DTEND;VALUE=DATE:20250503
DTSTAMP:20260414T211714
CREATED:20250305T140624Z
LAST-MODIFIED:20250305T140855Z
UID:10001468-1745798400-1746230399@ddsa.dk
SUMMARY:International School of Chemometrics 2025 - PROGRAMMING
DESCRIPTION:Aim and content \nFour week school designed to introduce different key aspects of Data Science and Machine Learning in different branches of science (chemistry\, food & feed\, physics\, environmental\, political economics\, etc). The course has the following modules: \n1) PROGRAMMING – Introduction to Programming for Multivariate data analysis in Matlab\, Python and R\nThis online seminar is based on online pre-recorded videos that are thought to be an introduction to the main aspects of dealing with Matlab\, R and Python focused on Multivariate Data Analysis. \n2) BASIC – Basic Introduction to Chemometrics and Linear Algebra\nThis seminar includes two parts:\nEXPLORE (PCA) and LINAL (Linear Algebra). \n3) INTERMEDIATE:\nThis seminar includes three parts:\n• DoE\, Design of Experiments\n• VARSEL\, variable selection methods;\n• MCR\, Multivariate Curve Resolution. \n4) DL – Non-Linear Modeling / Deep Learning\nThis seminar includes two parts: methods for non-linear modeling and the different architectures of Artificial Neural Networks (basic structures\, shallow neural networks and deep neural networks). \n5) Classification\nThe course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. It will also deal with Support Vectors Machine and Random Forests. \n6) Optimization\nThis seminar will give an overview of different optimization methods that are extremely useful for optimizing hyperparameters in models. Methods like Particle swarm optimization (PSO) or Gauss-Newton will be taught and different examples discussed. \n7) Metabolomics\nThis seminar will deal with the chemometric approaches for integrating (“fusing”) data from different sources. First of all\, the various configurations which may occur when dealing with multiple data matrices will be presented and discussed\, and a hierarchy/systematization of the possible data fusion approaches will be introduced. The main multi-block strategies for data exploration and predictive modeling will then be discussed and compared. Further classification of models depending on whether the globally common\, locally common and distinct information is considered or not will also be introduced. The theoretical and algorithmic description of the methods will be accompanied by worked examples of real data sets. \n8) GLUE (How not to make Chemometrics) and WORKSHOP\nWe will take a very close look at all the most common mistakes that even experienced people will do when doing multivariate analysis. We will cover exploration\, calibration\, interpretation\, visualization and many other subjects. This is done with a focus on the most common problems as well as sound alternatives to address them. \nFormal requirements \nNone required. We start from the basics and go all the way to advanced. \nLearning outcome \nKnowledge\nUpon completing the course\, students will:\n– Understand the foundational principles of data science methods\, specifically in chemometrics and multivariate analysis.\n– Gain theoretical knowledge in statistical and machine learning techniques such as PCA\, multivariate regression\, variable selection methods\, and non-linear modeling.\n– Comprehend advanced data analysis methods including multiway data analysis\, ANOVA Simultaneous Component Analysis (ASCA)\, and data fusion strategies.\n– Learn the main experimental designs used in Design of Experiments (DoE)\, their applications\, and their limitations.\n– Understand the implications of improper analysis in chemometrics and methods to avoid common mistakes in multivariate data analysis. \nSkills\nStudents will develop the ability to:\n– Apply data analysis techniques (PCA\, MCR\, etc.) to real-world data sets in their own research fields.\n– Code basic algorithms in Matlab\, Python\, or R for multivariate data analysis and create analytical data pipelines.\n– Perform experimental design using DoE principles to optimize processes and interpret data effectively.\n– Integrate data from multiple sources (Data Fusion) and interpret the resulting fused datasets to address complex research questions.\n– Conduct a critical analysis of data\, identifying and troubleshooting potential errors in data interpretation and visualization. \nCompetences\nBy the end of the course\, students will be able to:\n– Analyze and interpret diverse data types independently\, drawing meaningful conclusions from complex datasets.\n– Solve domain-specific data problems in a structured and reproducible manner.\n– Collaborate effectively with researchers from diverse scientific backgrounds\, communicating data science concepts clearly.\n– Assess and select appropriate data analysis methods based on the research context and the nature of the data. \nTarget group \nAll PhD students who aim to use data science within chemical and related areas. \nTeaching and learning methods \nThe seminars of the School of Chemometrics will consist of a mix of presentations from world leading researchers mixed with practical exercises in data analytic software that provides the student with practical experience on how to apply the tools learned in the course. The exercises are done under the supervision of the teachers. \nThe initial week on programming offers teaching in three different languages and all the teaching in this part is based on e-learning. The student can choose between either programming in MATLAB\, Python or R in this first week. \nThe rest of the school (three weeks) is physical on-site training. \nLecturers \n• Assoc. Prof. Davide Ballabio\, University of Milano-Bicocca will teach several days on classification and general data science\n• Assoc. Prof. Agnieszka Smolinska\, Maastricht University will teach design of experiment\n• Prof. José Amigo Rubio\, University of Basque Country will teach courses on programming\, basic chemometrics\, MCR and general data science. They are also the main responsible for the day-to-day activities throughout the course.\n• There are several other guest lectures but these are the ones for which we have applied for funding \nRemarks \nCourse fee: 6000 DKK for the full course. Fee covers additional invited teachers for the course\, social events\, facilities for poster. \nPhD students enrolled at a Danish PhD school that is a member of the open market for PhD courses: free of charge. \nMaster’s students from Danish universities: free of charge. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2025-programming-1-ects/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250127
DTEND;VALUE=DATE:20250201
DTSTAMP:20260414T211714
CREATED:20240821T085610Z
LAST-MODIFIED:20240821T085610Z
UID:10001341-1737936000-1738367999@ddsa.dk
SUMMARY:Fundamentals of the PhD education at SCIENCE  - module 2- K3
DESCRIPTION:Aim and content \nThe purpose of the module is to introduce the students into Data Science\, Data Management\, and Career Management: \n•	Many students will apply/develop Data Science methods (data analysis\, statistics\, and machine learning) directly in their own research; and 		all students should be aware of this potential. •	Some students will directly apply Data Management principles and all students should be aware of the general policies.•	All students should actively manage their careers.  \nThe module consists of 10 morning/afternoon sessions during the on-campus module week (7 on Machine Learning and Statistics\, 2 on Data Management\, and 1 on Career Management).  \nThe Data Science element will aim to ensure that PhD students will consider Data Science as a methodology and allow them to apply basic Statistics and Machine Learning methods when appropriate in their research. The element will cover:•	Machine learning foundations and methods•	Statistics foundations and methods•	Programming environments for Statistics and Machine Learning•	Data Science caveats and best practice•	Introduction into AI and high-performance computing \nThe aim of the Data Management element is to equip PhD students with knowledge and skills to:•	manage data and primary materials responsibly during their PhD projects.•	create open and reproducible research outputs.  \nThe aim of the Career Management element is that PhD students start to explore their values\, motivation\, and the great variety of career options that are open to them after their PhD. The element will cover:•	Megatrends in the labour market and typical career paths for PhDs from the natural sciences•	Motivation\, values\, and career priorities•	Change of scientific environment•	Networking for career development \nLearning outcome \nThe learning outcomes for the three elements are given below. Each outcome is marked K for Knowledge\, S for Skills\, or C for Competences.  \nData Science Element:•	Know Data Science as a research methodology (K). •	Apply basic Statistics and Machine Learning computational frameworks and methods when appropriate in their research (S).•	Build a network for potential inter-disciplinary data science collaborations (C).  \nData Management Element:•	Identify relevant legislation\, requirements\, and policies on data management applicable to research projects at UCPH (K).•	Recognize recommendations and requirements regarding open and reproducible research designs\, data collection\, and data publication (K).•	Classify data and conduct a risk assessment to ensure the secure storage of data (S).•	Assess how data can be preserved and shared to guarantee FAIR use of data (S).•	Assess when to use electronic lab notes (S).•	Apply recommendations and requirements regarding open and reproducible research designs\, data collection\, and data publication (S).•	Contribute to planning and conducting appropriate data and materials management in all phases of their PhD project (C). •	Be able to adhere to best practices of open and reproducible research (C). \nCareer Management Element:•	Differentiate typical career paths for SCIENCE PhDs (K).•	Describe selected understandings of motivation (K). •	Explore and explain their career priorities (S).•	Build a professional profile on LinkedIn (S).•	Assess how different uses of their change of scientific environment may impact their career (S).•	Integrate knowledge of typical career paths for SCIENCE PhDs with an understanding of their personal career priorities (C).•	Develop a personal networking strategy and build a professional network that supports their career interests (C). \nLecturers \nThe Data Management element may include guest lectures on reproducibility\, electronic lab notebooks\, and GDPR.  \nRemarks \nThe PhD School at the Faculty of SCIENCE is committed to building a learning environment that welcomes\, includes\, and empowers all its PhD students. By building a Faculty-wide peer community of PhD-students with the Fundamentals course\, we secure that all PhD candidates are given adequate instruction in a range of essential competences that lie outside the core scientific research skills offered through supervision\, tool-box and specialized PhD courses. Moreover\, we build bridges between different research programmes at the Faculty of SCIENCE and offer diverse\, multidisciplinary fora of exchange strengthening the PhD candidates’ scientific and social networks and laying the foundation for a strong alumni culture. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/fundamentals-of-the-phd-education-at-science-module-2-k3/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250122
DTEND;VALUE=DATE:20250201
DTSTAMP:20260414T211714
CREATED:20241002T132257Z
LAST-MODIFIED:20241002T132257Z
UID:10001399-1737504000-1738367999@ddsa.dk
SUMMARY:Python for SCIENCE
DESCRIPTION:Content \nThis course is an official Toolbox course at SCIENCE-UCPH and a generic course under the Danish PhD regulations. The course introduces the dominant programming language in data science\, Python. Python is a general-purpose programming language that is currently being used in many active data science projects with open-source libraries available. \nThe workshop will teach the basic programming constructs in Python and then provide data science examples\, including data import\, visualization\, and analysis. We will introduce integrated development interfaces such as jupyter. We will introduce libraries from active open-source frameworks (numpy\, pandas\, matplotlib\, sklearn\, …). \nThe course is aimed at PhD students\, who need tools for data exploration\, data analysis\, and data visualization. Post Docs\, Professors\, and Master’s thesis students from SCIENCE may register for participation and will be accepted if space permits. \nFormel requirements \nThe number of participants is limited at 50\, and priority will be given to PhD students from UCPH-SCIENCE. \nLearning outcome \nAfter course completion\, the students are expected to be able to: \nKnowledge:– Understand computational thinking concepts.– Understand key programming elements (e.g. variables\, objects\, functions\, modules).– Know useful open-source libraries (e.g. pandas\, matplotlib\, sklearn). \nSkills:– Develop/adapt/extend a computer-based software program for analysis of relevant data.– Apply good development principles.  \nCompetences:– Propose relevant analysis methods for scientific data science problems.– Consider cross-disciplinary data science methods in their research. \nLiterature \nCourse lecture slides and exercises. We will use data\, examples\, and other material from publicly available sources.  \nTeaching and learning methods \nThe course is composed of sessions combining lectures and exercises. For each topic\, the students will get hands-on experience in applying\, modifying\, and programming analysis methods.  \nLecturers \nJulius B. Kierkegaard\, Tenure-Track Assistant Professor\, DIKUOswin Krause\, Associate Professor\, DIKU \nRemarks \nParticipants from SCIENCE are exempt from the course fee\, for all other participants the course fee is DKK 3600.  \nFor details for this and other Data Science Lab courses\, see: http://datalab.science.ku.dk/english/course/ \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/python-for-science/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241202
DTEND;VALUE=DATE:20241207
DTSTAMP:20260414T211714
CREATED:20240821T085641Z
LAST-MODIFIED:20240821T085641Z
UID:10001352-1733097600-1733529599@ddsa.dk
SUMMARY:Multivariate Data Analysis - 2.5 ECTS - 2024
DESCRIPTION:Aim and content \nIn industry and research huge amounts of physical\, chemical\, sensory and other quality measurements are produced on all sorts of materials\, processes and products. Exploratory data analysis / chemometrics offers a tool for extracting the optimal information from these data sets through the use of digitalization (modern software and computer technology). \nThe course will give a step-by-step theoretical introduction to exploratory data analysis / chemometrics supported by practical examples from food science\, environmental science\, pharmaceutical science etc. \nMethods for exploratory analysis (Principal Component Analysis)\, multivariate calibration (Partial Least Squares) and basic data preprocessing are considered. The mathematics behind most of the concepts will be given together with the practical applications and considerations of the methods. \nEven more important\, though\, is the understanding and interpretation of the computed models. As is methods for outlier detection and model validation. Computer exercises and cases will be performed applying user-friendly software. A thorough introduction to the software will be given. \nCourse content: \n•	Introduction to Multivariate Data Analysis•	Principal Component Analysis (PCA)•	Pre-processing•	Outlier detection•	Partial Least Squares Regression (PLSR)•	Validation•	Variable selection \nFormel requirements \nBasic statistical knowledge.  \nLearning outcome \nKnowledge:•	Describe chemometric methods for multivariate data analysis (exploration and regression)•	Describe techniques for data pre-preprocessing•	Describe techniques for outlier detection•	Describe method validation principles•	Understand the basics of the algorithms behind the PCA and PLS•	Understand the math of data pre-processing \nSkills:•	Apply theory on real life data analytical cases•	Apply commercial software for data analysis•	Interpret multivariate models (both exploratory and regression) \nCompetences:•	Discuss and respond to univariate versus multivariate data analytical methodology in problem solving in society \nTarget group \nPhD students from any scientific field that gather data with several samples (+10) and many variables (+10). \nTeaching and learning methods \nThere will be a mixture of several different teaching methodologies:–	Lectures (most also available as videos)–	Exercises + Walkthrough–	Short cases + Fish tank–	Day cases + Debriefing sessions–	Visual examples \nRemarks \nResponsible for scientific course content: Åsmund Rinnan\, aar@food.ku.dk. \nCollaborating departments at University of Copenhagen: PLEN and CHEM. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/multivariate-data-analysis-2-5-ects-2024/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241111
DTEND;VALUE=DATE:20241130
DTSTAMP:20260414T211714
CREATED:20240821T085941Z
LAST-MODIFIED:20240821T085941Z
UID:10001335-1731283200-1732924799@ddsa.dk
SUMMARY:From Point to Pixel: A Toolbox for Spatial Analysis and Mapping in Environmental Science
DESCRIPTION:Aim and content \nThis PhD course\, “From Point to Pixel\,” is designed to equip students with the essential tools and skills needed to transform ground truth sample data into high-resolution pixel-level estimates\, which can then be aggregated into comprehensive maps. The primary goal of the course is to provide students with the expertise necessary to quantify the spatial landscape accurately. These maps serve as crucial resources for making informed decisions related to\, for example\, biodiversity conservation or facilitating the transition to a greener society through sustainable forest management. \nKey Course Objectives: \nGround Truth Data Handling: Students will learn how to develop a sampling strategy\, collect\, organize\, and preprocess ground truth observations efficiently. This includes data cleaning\, quality control\, and geospatial data handling techniques. \nAdvanced Modeling Techniques: \nThe course will delve into advanced modeling methods that allow students to establish robust relationships between ground truth observations and remotely sensed variables. This includes statistical modeling\, machine learning\, and geospatial modeling approaches. \nPixel-Level Estimation: \nStudents will be introduced to relevant RS data to be used in combination with ground truthing and machine learning to generate pixel-level estimates\, enabling them to produce high-resolution maps that accurately represent the environmental parameters under study. \nSpatial Landscape Mapping: \nStudents will learn how to aggregate pixel-level estimates into comprehensive maps\, providing a detailed quantification of the spatial landscape. These maps can be used for various applications\, such as assessing habitat quality for biodiversity and estimating available wood resources for a sustainable green transition. \nBy the end of “From Point to Pixel\,” participants will possess a powerful toolbox of techniques and methodologies to produce accurate\, high-resolution maps that aid in biodiversity conservation\, sustainable wood resource management\, and other critical aspects of environmental science. This course equips students with the skills needed to make data-driven decisions and contribute to the development of a more sustainable and environmentally conscious society. \nLearning outcome \nKnowledge: \n\n… of remotely sensed datatypes and their strength and weaknesses.\n… of ground truth sampling designs and methodologies.\n… of methods for statistical modeling\, machine learning\, and geospatial modeling approaches.\n\n Skills: \n\nTo develop sampling strategies.\nTo collect\, organize\, and preprocess ground truth observations efficiently.\nTo generate maps from pixel-based remote sensing.\nTo make data-driven decisions for a sustainable society.\n\nCompetences: \n\nGround Truth Data Handling: Students will gain proficiency in developing effective sampling strategies\, collecting\, organizing\, and preprocessing ground truth observations efficiently.\nParticipants will acquire expertise in employing advanced modeling methods to establish robust relationships between ground truth observations and remotely sensed variables.\nThe course will equip students with the knowledge and skills necessary to utilize remote sensing data in conjunction with ground truthing and machine learning techniques to generate precise pixel-level estimates.\nParticipants will learn how to aggregate pixel-level estimates into comprehensive maps\, enabling detailed quantification of the spatial landscape. This involves understanding spatial patterns and processes\, as well as techniques for synthesizing and visualizing complex geospatial information. \n\nTeaching and learning methods \nA variety of teaching and learning methods are applied to reach the goals of the course: \nLectures: \nWe use lectures to provide students with tools necessary to engage in the practical exercises and project work. \nPractical exercises:  \nThroughout the course\, students will work on real-world case studies and projects to apply their knowledge and skills to solve environmental challenges. \nProject work: \nWe encourage students to bring a case study that they wish to work with during the course. \nType af assessment: \n Written assignment on own project or project handed out by the course responsible must be completed and approved by the course responsible. \nThere will be a course fee of DKK 700. The fee covers a course dinner during the lecture week and snacks\, fruit\, and coffee/tee served during course days. Lunch will be self-organized. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/from-point-to-pixel-a-toolbox-for-spatial-analysis-and-mapping-in-environmental-science/
LOCATION:Department of Geoscience and Natural Resource Management
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240826
DTEND;VALUE=DATE:20240831
DTSTAMP:20260414T211714
CREATED:20240424T084146Z
LAST-MODIFIED:20240424T084146Z
UID:10001172-1724630400-1725062399@ddsa.dk
SUMMARY:Advanced Measurements and Analyses of Greenhouse Gas Fluxes from Soils and Ecosystems (AMAGS)
DESCRIPTION:Aim and content \nIt is critical for environmental scientists to quantify the major sources and sinks of the most important greenhouse gases (GHGs)\, CO2\, CH4 and N2O\, as well as to disentangle the processes involved in GHG production and consumption in terrestrial ecosystems. Such data and knowledge are essential for the development of national and international strategies for improved/optimized land use and climate mitigation. The course aims to teach future researchers how to use the newest\, manual and automated chamber technologies and state-of-the-art analytical tools for measuring and interpreting the GHG exchange between ecosystems and the atmosphere. \nThe chamber method is the most widely used for GHG flux measurements between ecosystems and the atmosphere: However\, manual chamber measurements are time consuming\, which hampers spatial and temporal data coverage and implementation of results in a larger context. Recent development in combining novel chamber designs with real-time GHG analyses now allows for automation of GHG flux measurements leading to a hundredfold increase in the number of measurements per unit of time. This technological development improves the temporal representation and resolution in data\, in turn helping researchers to improve their understanding of the soil and ecosystem processes governing the exchange of greenhouse gases with the atmosphere at temporal and spatial scales previously out of reach. On the other hand\, the much larger data sets produced with automated measurements also creates a need for automating data analytical procedures and quality control.  \nThe course will focus on developing the skill set for post-graduate students in measuring and analyzing the exchange of GHG’s between the soil/ecosystem and the atmosphere using newest chamber technologies. The course will highlight the conceptual\, technological and analytical challenges involved in obtaining the “true” measure of the GHG flux between an ecosystem and the atmosphere and how these data can be used to address fundamental knowledge gaps related to the processes involved in ecosystem GHG production and uptake and potential ecosystem feedback to climate and global changes. \nLearning outcome \nKnowledge:• describe commonly used chamber methods and equipment for measuring greenhouse gas fluxes from soils• demonstrate the field use of the chamber method • discuss theory of sampling design \nSkills:• work independently with the chamber methods under field conditions • evaluate the pros and cons of using specific designs to measure greenhouse gas fluxes• apply the sampling methodology in the field• design a problem-oriented scientific field sampling protocol for greenhouse gas fluxes \nCompetences:• project-oriented group work in the field• choose the correct techniques to obtain a representative flux of greenhouse gases in space and time• analyze field data using graphic and statistical techniques (R software)• synthesize results in a written report \nTeaching and learning methods \nThe student prepares for the onsite course by reviewing current GHG flux literature prior to course start. The course preparation involves e-learning including pre-recorded lectures and a questionnaire\, aimed to form the basis for an active involvement in the specific theoretical and methodological problems\, how to construct a research question and carry out a field sampling design. During the course\, students will be working with hands-on measurements at various field sites and in the lab using different manual and automated chamber measurement systems. The insights from the hands-on exercises will form the basis for classroom discussion and learning. Finally\, the students will perform hands-on analytical work in the classroom using R software on the obtained data in combination with long-term data from automatic chambers. \nThroughout the course\, the teacher team presents lectures covering the central theoretical and practical aspects of the chamber methodology. Lectures interact with class instructions for the theoretical and practical exercises that are in focus on the course. The students will furthermore actively engage in the course by presenting their current PhD projects as well as through group work in theoretical exercises and fieldwork. The course ends with group presentations on a chosen topic covering both theoretical aspects and actual results obtained during the course. Each group further summarizes their work in a written report submitted no later than two weeks after the course presenting and discussing the collected data and results. The participants pass the course after approval of their written report no later than 2 weeks after submission.  \nType of assesment \nThe course is finalized by a written report submitted max. 14 days after the course. \nGuest lecturersJohannes W.M. Pullens is assistant Professor at Dept. of Agroecology at Aarhus University. He is involved in the AnaEE Denmark research Infrastructure and works with both eddy covariance and chamber measurements to measure exchange of CO2\, N2O and CH4 of agrosystems. He will contribute with lectures in lecture room and in the field as well as to practical exercises throughout the week of the course. \nSander Bruun (assoc prof) and Azeem Tariq (assist prof) from Dept. of Plant and Environmental Sciences at University of Copenhagen are also involved in AnaEE Denmark activities with measurements of GHG exchange in agricultural systems. They will participate with lectures and be responsible for the field visit to the Højbakkegård site during the course. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/advanced-measurements-and-analyses-of-greenhouse-gas-fluxes-from-soils-and-ecosystems-amags/
LOCATION:Department of Geoscience and Natural Resource Management
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240805
DTEND;VALUE=DATE:20240811
DTSTAMP:20260414T211714
CREATED:20240424T084333Z
LAST-MODIFIED:20240424T084333Z
UID:10001176-1722816000-1723334399@ddsa.dk
SUMMARY:Geometric Morphometrics in R
DESCRIPTION:Content \nThe field of geometric morphometrics (GM) is concerned with the quantification and analysis of patterns of shape variation and its covariation with other variables. Advances in the statistical treatment of morphometric data\, made largely possible by the course instructors\, have led to a revolution in GM that can be seen across scientific disciplines\, including paleontology\, ecology\, evolutionary biology\, evo-devo\, and more. \nProgram: \nMondayDigital Image Acquistion: GBIF\, MorphoSourceImage Naming & OrganisationData CovariablesLandmark Export & Formats \nTuesdayMorphometrics: History\, Introduction and Data TypesLinear Algebra and Linear ModelsSuperimposition: Generalized Procrustes Analysis (GPA)Laboratory Tutorial-Individual Research \nWednesdayShape Spaces\, Shape Variables\, & PCAGPA with SemilandmarksIntroduction to gmShinyLaboratory Tutorial-Individual Research \nThursdayShape Statistics IAllometryShape Statistics II o Laboratory Tutorial-Individual Research \nFridayPhylogenetics and Shape VariationSymmetry and AsymmetryDisparityIntegration and ModularityLaboratory Tutorial-Individual Research \nSaturdayMissing DataFuture Directions and ProspectusStudent PresentationsSocial Event (Group Dinner \nAim and content \nThe goal of this workshop is to provide participants with both working knowledge of the theory of geometric morphometrics\, as well as practical training in applying these methods using R.Course content over 6 days consists of morning lectures (Digitisation\, Superimposition\, Shape space\, Shape statistics\, Phylogenetic integration\, Future directions) followed by afternoon hands-on practical exercises and opportunities for one-on-one training. The final day will end with student presentations and a social event (group dinner).See attached Course Content for more details. \nFormel requirements \nEnrolled in or completed PhD program in Natural or Computer Sciences. It is assumed that participants have some working knowledge in R\, as the practical sessions will focus on geometric morphometric analyses and not basic R use. It is therefore strongly recommended that participants refresh their R skills prior to attending the workshop \nLearning outcome \nKnowledge:• Distinguish between data types used in shape analysis• Understand linear algebra and models underlying GM \nSkills:• Collect digital shape data from online sources• Place landmarks on 2D and 3D images• Perform Procrustes Superimposition • Visualise shape differences using PCA and other tools• Perform shape statistics in R environment \nCompetences:• Use geomorph software in R• Produce statistical models for analysing and comparing shapes \nLiterature \nSoftware Packages:Baken\, E.K.\, M.L. Collyer\, A. Kaliontzopoulou\, and D.C. Adams. 2021. geomorph v4.0 and gmShiny: enhanced analytics and a new graphical interface for a comprehensive morphometric experience. Methods in Ecology and Evolution. 12:2355–2363.Collyer\, M.L\, and D.C. Adams. 2018. RRPP: An R package for fitting linear models to high-dimensional data using residual randomization. Methods in Ecology and Evolution. 9:1772-1779.General Overview of Geometric Morphometrics:Adams\, D.C.\, F. J. Rohlf\, and D.E. Slice. 2013. A field comes of age: Geometric morphometrics in the 21st century. Hystrix. 24:7-14. \nTarget group \nThe course is aimed at students working with digital image data who wish to learn the latest methods for quantifying and comparing 2D and 3D shapes in a statistical framework. Applications include\, but are not limited to\, medical image analysis\, ecology\, morphology\, evolutionary biology\, paleontology\, anthropology and phylogenetics. \nTeaching and learning methods \nThe course is organized in morning theoretical and afternoon practical sessions. The lectures provide a solid theoretical understanding of the mathematical underpinnings of the procedures for proper analysis of shape data from landmark coordinates. Laboratory sessions put these concepts into practice\, and include worked examples\, giving participants the opportunity to gain hands-on experience in the treatment of shape data using the R package geomorph. \nLecturers \nThe two guest lecturers are the founding authors of the R package geomorph\, cited over 3000 times in the scientific literature (Dean Adams\, Iowa State University; Michael Collyer\, Chatham University\, USA). They have taught 20 versions of this sought-after workshop since 2001 in countries around the world. Guest lecturer/collaborator Christy Hipsley (Biology\, KU) is an expert in 3D digitisation and will teach Day 1 on image acquisition and landmark placement \nRemarks \nNo fee for students completing the course. Student participation must bein agreement with the principal supervisor. \nSigning up: Please note that in addition to your online registration at this page\, we also need you to send your name\, address\, title of your PhD Project including start and end date\, and a few lines describing the relevance of this course to your studies to christy.hipsley@bio.ku.dk before May 20\, 2024. \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/geometric-morphometrics-in-r/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240624
DTEND;VALUE=DATE:20240629
DTSTAMP:20260414T211714
CREATED:20240424T085746Z
LAST-MODIFIED:20240424T085746Z
UID:10001141-1719187200-1719619199@ddsa.dk
SUMMARY:2nd Copenhagen School of Stochastic Programming
DESCRIPTION:Content \nThis course provides a rigorous and research-oriented introduction to stochastic programming\, a mathematical framework for decision-making in the presence of uncertainty. In many real-life problems\, important information is unknown to the decision-maker and only distributional information is available. Examples include the scheduling of power generation while demand and renewable production is uncertain\, investments in assets with uncertainty in future returns or production of goods for which demand is stochastic.  \nThe course will start by formalizing such decision problems as mathematical optimization problems and analyzing their fundamental properties. From a computational perspective\, these problems may be extremely challenging. Thus\, a major part of the course will discuss approximations\, either of the underlying distributions or of the optimization problem itself. The former involves so-called scenario generation and stability of the optimization problems. The latter covers various approximation and bounding techniques. The course will proceed with a number of applications in the energy sector\, an area for which stochastic programming has become increasingly important with the adoption of intermittent renewable energy sources. Finally\, a selection of solution methods will be addressed\, including exact decomposition procedures and approximate methods with strong connections to emerging approaches in machine learning.  \nFormel requirements \nPrerequisites:A solid understanding of linear programming theory and some knowledge of probability theory (e.g.\, understanding what probability distributions are for both continuous and discrete random variables).  \nLearning outcome \nThe students will become well acquainted with the theory of stochastic programming and the challenges involved when applying stochastic programming in practice. Particularly\, upon completion of the course\, the students will be able to formulate two-stage and multi-stage stochastic programs\, analyze their properties and discuss their practical implications. They will also learn how to approximate these problems\, generate scenarios and address stability with respect to these\, bound and assess the value of stochastic optimization. Finally\, they will be able to apply and adapt selected traditional and novel solution methods. \nLiterature \nLecture notes and hand-ins provided by the organizers of the course.. \nTeaching and learning methods \nEvery day consists of three hours of lectures and two hours of exercises or project work on the same topic.  \nRemarks \nPlease read more and SIGN UP here: \nhttps://www.math.ku.dk/english/calendar/events/cssp_2/ \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/2nd-copenhagen-school-of-stochastic-programming/
LOCATION:Department of Mathematical Sciences                Universitetsparken 5
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240606
DTEND;VALUE=DATE:20240607
DTSTAMP:20260414T211714
CREATED:20240424T091611Z
LAST-MODIFIED:20240424T091611Z
UID:10001158-1717632000-1717718399@ddsa.dk
SUMMARY:International School of Chemometrics 2024 - MW - Multi Way Analysis
DESCRIPTION:Aim and content \nThe “International School of Chemometrics (ISC) – 2024” is a four-week PhD school specifically aimed at people having acquired some basic understanding of chemometrics. ISC will be offered at practitioner level. \n“ISC-2024” is addressed to PhD students/post-docs\, associate professors\, etc. who want to acquire further knowledge on advanced multivariate data analysis from different disciplines (Chemistry\, Physics\, Food Science\, Biology\, Geology\, Environmental Sciences\, etc.). ISC also addresses companies or research laboratories who want to implement advanced multivariate data analysis in their daily research environment. \nISC aims at being a platform for: \n– Learning data analysis methods. ISC is specifically designed for researchers who want to acquire extra knowledge on multivariate data analysis and adapt it in their routine work. \n– Sharing knowledge and interchange of ideas between students covering different scientific backgrounds. One of the key points of the course is the interaction between the students and troubleshooting – always within the framework of scientific data analysis and performance. \n– Meeting world-wide recognized experts of Multivariate Data Analysis in an open discussion forum environment. ISC will count with teachers that are well-recognized experts on chemometrics and multivariate data analysis in their respective fields. \nThis\, at the same time\, will offer the possibility of opening new collaborative frameworks between students and teachers. The students enrolling will have the opportunity of choosing the seminars that they consider most relevant\, without the obligation of attending a minimum amount of seminars. \nSeminars and dates:Each seminar is independent and the registration is individual. The students can choose to attend the seminars which they consider more relevant for their research. There is no minimum of seminars that the student must attend. \n1 – PROGRAMMING (José Manuel Amigo\, Sergey Kucheryavskyi and Anders Krogh Mortensen): This online prerecorded videos are thought to be an introduction of the main aspects dealing with Matlab\, R and Python. Check the “detailed information website” for more information. 1 ECTS. Start the 13th of May\, 2024. \n2 – BASIC (José Manuel Amigo and Morten Rasmussen): This seminar includes 2 parts. EXPLORE and LINAL. Basic introduction to Chemometrics. Data types\, PCA\, pre-processing\, Multivariate Regression\, Linear Algebra for Multivariate Data Analysis. 5 days. 2.5 ECTS. 20 – 24 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n3 – INTERMEDIATE (Agnieszka Smolinska\, Åsmund Rinnan\, Anna de Juan): This seminar includes 3 parts. DoE (Design of Experiments)\, VARSEL (variable selection methods) and MCR (Multivariate Curve Resolution). 5 days. 2.5 ECTS. 27 – 31 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n4 – DEEP LEARNING (Jesper Løve Hinrich): This seminar is an introduction to non-linear methods and deep learning. 3 days. 1.5 ECTS. 3 – 5 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n5 – CLASS (Davide Ballabio): The course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine\, Random Forests and Self-Organized Maps Artificial Neural Networks. 4 days. 2 ECTS. 3 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n6 – DATA FUSION (Federico Marini): Data Fusion is gaining extreme importance in science. This seminar is an introduction to the main methods to fuse your data. 2 days. 1 ECTS. 5 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n7 – MULTIWAY (Beatriz Quintanilla): Methods like PARAFAC\, PARAFAC2 and N-PLS will be taught. 1 day. 0.5 ECTS. 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n8 – GLUE (José Manuel Amigo\, Rasmus Bro\, Federico Marini\, Davide Ballabio): How NOT to Make Chemometrics and afternoon workshop to talk about your particular needs. 1 day. NO ECTS. 7 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \nExtremely important note: The last week\, the seminars are simultaneous. Therefore:– Attending DL means that you cannot sign up for CLASS and DF.– Attending CLASS means that you cannot sign up for DL and DF.– Attending DF means that you cannot sign up for DL\, CLASS and MW.– Attending MW means that you cannot sign up for CLASS and DF.Despite this fact\, attending any of the seminars of the last week will grant you access to the material of all the seminars of that week. \nFurther information:https://www.hypertools.org/isce-mail of ISC (ischemometricshelp@gmail.com) \nMW – Multi Way analysis \nMulti-way data is gaining popularity due to the capability of scientific devices to generate data with\, at least\, 3 dimensions (elution time – mz channel – samples\, excitation-emission – sample\, etc). Therefore\, learning the basics of multi-way analysis will help to extract the most of that complex data structure. In this sense\, methods like parallel factor analysis (PARAFAC) and PARAFAC2 will be studied and applied to different examples. \nDates and timetable: 22nd of June\, 2023. From 9 am until 4 pm (CET) with 1-hour lunch break (6 hours) \nPrevious knowledge needed: Basic multivariate data analysis \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with:– Matlab– PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO\, a fully functional demo will be available for the School. \nTeacher: Beatriz Quintanilla \nLearning outcome \nThe intended learning outcomes are divided into two categories: \n1) Individual learning outcomes: the main target for each individual seminar is to learn the basis of one data analysis method focused on several proposed examples. The students have to be able to apply the acquired knowledge to any problem related to their own research. \n2) Global learning outcomes: Students attending all the seminars will\, at the end of the course\, be able to understand the structure of a vast amount of data structures and also to understand the problems derived from the data. Moreover\, they will be independent in the application of solutions to their problems in a dedicated manner. \nRemarks \nThe price for participating is as follows (same price for online and physically present): \nAcademia: DKK 600 per ECTS (approx. EUR 81 / USD 98)Industry: DKK 1500 per ECTS (aprox. EUR 200 / USD 245) \nPayment must be completed before the course starts. Information on the method of payment will be provided after confirmation of registration. \nAll seminars include all the material that the student might need:– Slides of the course (pdf).– Exercises– Datasets– Toolboxes– Refreshment during the lessons (coffee\, tea\, candies\, cookies\, and other amenities)– We do NOT provide: Matlab and lunch \nBest Poster Award for physically present attendants:In order to foment collaboration between us and interchange ideas\, ISC – 2024 will include different poster sessions. Therefore\, we would like to invite you to submit an abstract to the school. The abstract and poster are intended to promote the interchange of ideas between us\, students and teachers. See further instruction https://www.hypertools.org/isc \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2024-mw-multi-way-analysis/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240605
DTEND;VALUE=DATE:20240607
DTSTAMP:20260414T211714
CREATED:20240424T091427Z
LAST-MODIFIED:20240424T091427Z
UID:10001157-1717545600-1717718399@ddsa.dk
SUMMARY:International School of Chemometrics 2024 - DF - Data Fusion
DESCRIPTION:Aim and content \nThe “International School of Chemometrics (ISC) – 2024” is a four-week PhD school specifically aimed at people having acquired some basic understanding of chemometrics. ISC will be offered at practitioner level. \n“ISC-2024” is addressed to PhD students/post-docs\, associate professors\, etc. who want to acquire further knowledge on advanced multivariate data analysis from different disciplines (Chemistry\, Physics\, Food Science\, Biology\, Geology\, Environmental Sciences\, etc.). ISC also addresses companies or research laboratories who want to implement advanced multivariate data analysis in their daily research environment. \nISC aims at being a platform for: \n– Learning data analysis methods. ISC is specifically designed for researchers who want to acquire extra knowledge on multivariate data analysis and adapt it in their routine work. \n– Sharing knowledge and interchange of ideas between students covering different scientific backgrounds. One of the key points of the course is the interaction between the students and troubleshooting – always within the framework of scientific data analysis and performance. \n– Meeting world-wide recognized experts of Multivariate Data Analysis in an open discussion forum environment. ISC will count with teachers that are well-recognized experts on chemometrics and multivariate data analysis in their respective fields. \nThis\, at the same time\, will offer the possibility of opening new collaborative frameworks between students and teachers. The students enrolling will have the opportunity of choosing the seminars that they consider most relevant\, without the obligation of attending a minimum amount of seminars. \nSeminars and dates:Each seminar is independent and the registration is individual. The students can choose to attend the seminars which they consider more relevant for their research. There is no minimum of seminars that the student must attend. \n1 – PROGRAMMING (José Manuel Amigo\, Sergey Kucheryavskyi and Anders Krogh Mortensen): This online prerecorded videos are thought to be an introduction of the main aspects dealing with Matlab\, R and Python. Check the “detailed information website” for more information. 1 ECTS. Start the 13th of May\, 2024. \n2 – BASIC (José Manuel Amigo and Morten Rasmussen): This seminar includes 2 parts. EXPLORE and LINAL. Basic introduction to Chemometrics. Data types\, PCA\, pre-processing\, Multivariate Regression\, Linear Algebra for Multivariate Data Analysis. 5 days. 2.5 ECTS. 20 – 24 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n3 – INTERMEDIATE (Agnieszka Smolinska\, Åsmund Rinnan\, Anna de Juan): This seminar includes 3 parts. DoE (Design of Experiments)\, VARSEL (variable selection methods) and MCR (Multivariate Curve Resolution). 5 days. 2.5 ECTS. 27 – 31 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n4 – DEEP LEARNING (Jesper Løve Hinrich): This seminar is an introduction to non-linear methods and deep learning. 3 days. 1.5 ECTS. 3 – 5 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n5 – CLASS (Davide Ballabio): The course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine\, Random Forests and Self-Organized Maps Artificial Neural Networks. 4 days. 2 ECTS. 3 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n6 – DATA FUSION (Federico Marini): Data Fusion is gaining extreme importance in science. This seminar is an introduction to the main methods to fuse your data. 2 days. 1 ECTS. 5 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n7 – MULTIWAY (Beatriz Quintanilla): Methods like PARAFAC\, PARAFAC2 and N-PLS will be taught. 1 day. 0.5 ECTS. 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n8 – GLUE (José Manuel Amigo\, Rasmus Bro\, Federico Marini\, Davide Ballabio): How NOT to Make Chemometrics and afternoon workshop to talk about your particular needs. 1 day. NO ECTS. 7 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \nExtremely important note: The last week\, the seminars are simultaneous. Therefore:– Attending DL means that you cannot sign up for CLASS and DF.– Attending CLASS means that you cannot sign up for DL and DF.– Attending DF means that you cannot sign up for DL\, CLASS and MW.– Attending MW means that you cannot sign up for CLASS and DF.Despite this fact\, attending any of the seminars of the last week will grant you access to the material of all the seminars of that week. \nFurther information:https://www.hypertools.org/isce-mail of ISC (ischemometricshelp@gmail.com) \nDF – Data Fusion \nThe seminar will deal with the chemometric approaches for integrating (“fusing”) data from different sources. First of all\, the various configurations which may occur when dealing with multiple data matrices will be presented and discussed\, and a hierarchy/systematization of the possible data fusion approaches will be introduced. Then the main multi-block strategies for data exploration and predictive modeling will be discussed and compared\, and further classification of models depending on whether the globally common\, locally common and distinct information is considered or not will be introduced. The theoretical and algorithmic description of the methods will be accompanied by worked examples of real data sets. \nDates and timetable: 5th – 6th of June. From 9 am until 4 pm (CET) with 1-hour lunch break (12 hours) \nPrevious knowledge needed: Basic multivariate data analysis \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with:– Matlab \nTeacher: Federico Marini \nLearning outcome \nThe intended learning outcomes are divided into two categories: \n1) Individual learning outcomes: the main target for each individual seminar is to learn the basis of one data analysis method focused on several proposed examples. The students have to be able to apply the acquired knowledge to any problem related to their own research. \n2) Global learning outcomes: Students attending all the seminars will\, at the end of the course\, be able to understand the structure of a vast amount of data structures and also to understand the problems derived from the data. Moreover\, they will be independent in the application of solutions to their problems in a dedicated manner. \nRemarks \nThe price for participating is as follows (same price for online and physically present): \nAcademia: DKK 600 per ECTS (approx. EUR 81 / USD 98)Industry: DKK 1500 per ECTS (aprox. EUR 200 / USD 245) \nPayment must be completed before the course starts. Information on the method of payment will be provided after confirmation of registration. \nAll seminars include all the material that the student might need:– Slides of the course (pdf).– Exercises– Datasets– Toolboxes– Refreshment during the lessons (coffee\, tea\, candies\, cookies\, and other amenities)– We do NOT provide: Matlab and lunch \nBest Poster Award for physically present attendants:In order to foment collaboration between us and interchange ideas\, ISC – 2024 will include different poster sessions. Therefore\, we would like to invite you to submit an abstract to the school. The abstract and poster are intended to promote the interchange of ideas between us\, students and teachers. See further instruction https://www.hypertools.org/isc \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2024-df-data-fusion/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240603
DTEND;VALUE=DATE:20240607
DTSTAMP:20260414T211714
CREATED:20240424T084252Z
LAST-MODIFIED:20240424T084252Z
UID:10001173-1717372800-1717718399@ddsa.dk
SUMMARY:International School of Chemometrics 2024 - CLASS - Linear and Non Linear Classification
DESCRIPTION:Aim and content \nThe “International School of Chemometrics (ISC) – 2024” is a four-week PhD school specifically aimed at people having acquired some basic understanding of chemometrics. ISC will be offered at practitioner level. \n“ISC-2024” is addressed to PhD students/post-docs\, associate professors\, etc. who want to acquire further knowledge on advanced multivariate data analysis from different disciplines (Chemistry\, Physics\, Food Science\, Biology\, Geology\, Environmental Sciences\, etc.). ISC also addresses companies or research laboratories who want to implement advanced multivariate data analysis in their daily research environment. \nISC aims at being a platform for: \n– Learning data analysis methods. ISC is specifically designed for researchers who want to acquire extra knowledge on multivariate data analysis and adapt it in their routine work. \n– Sharing knowledge and interchange of ideas between students covering different scientific backgrounds. One of the key points of the course is the interaction between the students and troubleshooting – always within the framework of scientific data analysis and performance. \n– Meeting world-wide recognized experts of Multivariate Data Analysis in an open discussion forum environment. ISC will count with teachers that are well-recognized experts on chemometrics and multivariate data analysis in their respective fields. \nThis\, at the same time\, will offer the possibility of opening new collaborative frameworks between students and teachers. The students enrolling will have the opportunity of choosing the seminars that they consider most relevant\, without the obligation of attending a minimum amount of seminars. \nSeminars and dates:Each seminar is independent and the registration is individual. The students can choose to attend the seminars which they consider more relevant for their research. There is no minimum of seminars that the student must attend. \n1 – PROGRAMMING (José Manuel Amigo\, Sergey Kucheryavskyi and Anders Krogh Mortensen): This online prerecorded videos are thought to be an introduction of the main aspects dealing with Matlab\, R and Python. Check the “detailed information website” for more information. 1 ECTS. Start the 13th of May\, 2024. \n2 – BASIC (José Manuel Amigo and Morten Rasmussen): This seminar includes 2 parts. EXPLORE and LINAL. Basic introduction to Chemometrics. Data types\, PCA\, pre-processing\, Multivariate Regression\, Linear Algebra for Multivariate Data Analysis. 5 days. 2.5 ECTS. 20 – 24 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n3 – INTERMEDIATE (Agnieszka Smolinska\, Asmund Rinnan\, Anna de Juan): This seminar includes 3 parts. DoE (Design of Experiments)\, VARSEL (variable selection methods) and MCR (Multivariate Curve Resolution). 5 days. 2.5 ECTS. 27 – 31 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n4 – DEEP LEARNING (Jesper Løve Hinrich): This seminar is an introduction to non-linear methods and deep learning. 3 days. 1.5 ECTS. 3 – 5 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n5 – CLASS (Davide Ballabio): The course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine\, Random Forests and Self-Organized Maps Artificial Neural Networks. 4 days. 2 ECTS. 3 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n6 – DATA FUSION (Federico Marini): Data Fusion is gaining extreme importance in science. This seminar is an introduction to the main methods to fuse your data. 2 days. 1 ECTS. 5 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n7 – MULTIWAY (Beatriz Quintanilla): Methods like PARAFAC\, PARAFAC2 and N-PLS will be taught. 1 day. 0.5 ECTS. 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n8 – GLUE (José Manuel Amigo\, Rasmus Bro\, Federico Marini\, Davide Ballabio): How NOT to Make Chemometrics and afternoon workshop to talk about your particular needs. 1 day. NO ECTS. 7 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \nExtremely important note: The last week\, the seminars are simultaneous. Therefore:– Attending DL means that you cannot sign up for CLASS and DF.– Attending CLASS means that you cannot sign up for DL and DF.– Attending DF means that you cannot sign up for DL\, CLASS and MW.– Attending MW means that you cannot sign up for CLASS and DF.Despite this fact\, attending any of the seminars of the last week will grant you access to the material of all the seminars of that week. \nFurther information:https://www.hypertools.org/isce-mail of ISC (ischemometricshelp@gmail.com) \nCLASS – Linear and Nonlinear classification \nThe course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine\, Random Forests and Self-Organized Maps Artificial Neural Networks. The seminar consists of theoretical sessions together with a collection of exercises designed to understand the fundamentals of the three abovementioned methods. \nDates and timetable: 3th of June – 6th of June. From 9 am until 4 pm (CET) with 1-hour lunch break (12 hours) \nPrevious knowledge needed: Basic multivariate data analysis. \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with:– Matlab– The Classification toolbox can be freely downloaded from here: https://michem.unimib.it/download/matlab-toolboxes/classification-toolbox-for-matlab/ \nTeacher: Davide Ballabio \nLearning outcome \nThe intended learning outcomes are divided into two categories: \n1) Individual learning outcomes: the main target for each individual seminar is to learn the basis of one data analysis method focused on several proposed examples. The students have to be able to apply the acquired knowledge to any problem related to their own research. \n2) Global learning outcomes: Students attending all the seminars will\, at the end of the course\, be able to understand the structure of a vast amount of data structures and also to understand the problems derived from the data. Moreover\, they will be independent in the application of solutions to their problems in a dedicated manner. \nRemarks \nThe price for participating is as follows (same price for online and physically present): \nAcademia: DKK 600 per ECTS (approx. EUR 81 / USD 98)Industry: DKK 1500 per ECTS (aprox. EUR 200 / USD 245) \nPayment must be completed before the course starts. Information on the method of payment will be provided after confirmation of registration. \nAll seminars include all the material that the student might need:– Slides of the course (pdf).– Exercises– Datasets– Toolboxes– Refreshment during the lessons (coffee\, tea\, candies\, cookies\, and other amenities)– We do NOT provide: Matlab and lunch \nBest Poster Award for physically present attendants:In order to foment collaboration between us and interchange ideas\, ISC – 2024 will include different poster sessions. Therefore\, we would like to invite you to submit an abstract to the school. The abstract and poster are intended to promote the interchange of ideas between us\, students and teachers. See further instruction https://www.hypertools.org/isc \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2024-class-linear-and-non-linear-classification/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240603
DTEND;VALUE=DATE:20240606
DTSTAMP:20260414T211714
CREATED:20240424T090347Z
LAST-MODIFIED:20240424T090347Z
UID:10001156-1717372800-1717631999@ddsa.dk
SUMMARY:International School of Chemometrics 2024 - DL – Deep Learning
DESCRIPTION:Aim and content \nThe “International School of Chemometrics (ISC) – 2024” is a four-week PhD school specifically aimed at people having acquired some basic understanding of chemometrics. ISC will be offered at practitioner level. \n“ISC-2024” is addressed to PhD students/post-docs\, associate professors\, etc. who want to acquire further knowledge on advanced multivariate data analysis from different disciplines (Chemistry\, Physics\, Food Science\, Biology\, Geology\, Environmental Sciences\, etc.). ISC also addresses companies or research laboratories who want to implement advanced multivariate data analysis in their daily research environment. \nISC aims at being a platform for: \n– Learning data analysis methods. ISC is specifically designed for researchers who want to acquire extra knowledge on multivariate data analysis and adapt it in their routine work. \n– Sharing knowledge and interchange of ideas between students covering different scientific backgrounds. One of the key points of the course is the interaction between the students and troubleshooting – always within the framework of scientific data analysis and performance. \n– Meeting world-wide recognized experts of Multivariate Data Analysis in an open discussion forum environment. ISC will count with teachers that are well-recognized experts on chemometrics and multivariate data analysis in their respective fields. \nThis\, at the same time\, will offer the possibility of opening new collaborative frameworks between students and teachers. The students enrolling will have the opportunity of choosing the seminars that they consider most relevant\, without the obligation of attending a minimum amount of seminars. \nSeminars and dates:Each seminar is independent and the registration is individual. The students can choose to attend the seminars which they consider more relevant for their research. There is no minimum of seminars that the student must attend. \n1 – PROGRAMMING (José Manuel Amigo\, Sergey Kucheryavskyi and Anders Krogh Mortensen): This online prerecorded videos are thought to be an introduction of the main aspects dealing with Matlab\, R and Python. Check the “detailed information website” for more information. 1 ECTS. Start the 13th of May\, 2024. \n2 – BASIC (José Manuel Amigo and Morten Rasmussen): This seminar includes 2 parts. EXPLORE and LINAL. Basic introduction to Chemometrics. Data types\, PCA\, pre-processing\, Multivariate Regression\, Linear Algebra for Multivariate Data Analysis. 5 days. 2.5 ECTS. 20 – 24 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n3 – INTERMEDIATE (Agnieszka Smolinska\, Asmund Rinnan\, Anna de Juan): This seminar includes 3 parts. DoE (Design of Experiments)\, VARSEL (variable selection methods) and MCR (Multivariate Curve Resolution). 5 days. 2.5 ECTS. 27 – 31 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n4 – DEEP LEARNING (Jesper Løve Hinrich): This seminar is an introduction to non-linear methods and deep learning. 3 days. 1.5 ECTS. 3 – 5 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n5 – CLASS (Davide Ballabio): The course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine\, Random Forests and Self-Organized Maps Artificial Neural Networks. 4 days. 2 ECTS. 3 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n6 – DATA FUSION (Federico Marini): Data Fusion is gaining extreme importance in science. This seminar is an introduction to the main methods to fuse your data. 2 days. 1 ECTS. 5 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n7 – MULTIWAY (Beatriz Quintanilla): Methods like PARAFAC\, PARAFAC2 and N-PLS will be taught. 1 day. 0.5 ECTS. 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n8 – GLUE (José Manuel Amigo\, Rasmus Bro\, Federico Marini\, Davide Ballabio): How NOT to Make Chemometrics and afternoon workshop to talk about your particular needs. 1 day. NO ECTS. 7 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \nExtremely important note: The last week\, the seminars are simultaneous. Therefore:– Attending DL means that you cannot sign up for CLASS and DF.– Attending CLASS means that you cannot sign up for DL and DF.– Attending DF means that you cannot sign up for DL\, CLASS and MW.– Attending MW means that you cannot sign up for CLASS and DF.Despite this fact\, attending any of the seminars of the last week will grant you access to the material of all the seminars of that week. \nFurther information:https://www.hypertools.org/isce-mail of ISC (ischemometricshelp@gmail.com) \nDL – Non-Linear Modeling and Deep Learning \nThis seminar aims at providing a basic introduction to the techniques which may be used in all those situations when a linear relation is not enough to provide accurate results (e.g. due to the presence of multiple sources of variability). In this respect\, the most important aspects of data modeling will be considered (exploratory analysis\, classification and calibration). Topics such as kernel and dissimilarity-based approaches (including support vector machines)\, local modeling (kNN and locally weighted regression/classification)\, and different artificial neural network architectures (shallow learning and deep learning) will be covered.  \nDates and timetable: 3rd of June – 5th of June. From 9 am until 4 pm (CET) with 1-hour lunch break (30 hours) \nPrevious knowledge needed: Basic multivariate data analysis \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with:– Matlab and Python– PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO\, a fully functional demo will be available for the School.– Deep Learning Toolbox of Matlab \nTeacher: Jesper Løve Hinrich (20th and 21st of June). \nLearning outcome \nThe intended learning outcomes are divided into two categories: \n1) Individual learning outcomes: the main target for each individual seminar is to learn the basis of one data analysis method focused on several proposed examples. The students have to be able to apply the acquired knowledge to any problem related to their own research. \n2) Global learning outcomes: Students attending all the seminars will\, at the end of the course\, be able to understand the structure of a vast amount of data structures and also to understand the problems derived from the data. Moreover\, they will be independent in the application of solutions to their problems in a dedicated manner. \nRemarks \nThe price for participating is as follows (same price for online and physically present): \nAcademia: DKK 600 per ECTS (approx. EUR 81 / USD 98)Industry: DKK 1500 per ECTS (aprox. EUR 200 / USD 245) \nPayment must be completed before the course starts. Information on the method of payment will be provided after confirmation of registration. \nAll seminars include all the material that the student might need:– Slides of the course (pdf).– Exercises– Datasets– Toolboxes– Refreshment during the lessons (coffee\, tea\, candies\, cookies\, and other amenities)– We do NOT provide: Matlab and lunch \nBest Poster Award for physically present attendants:In order to foment collaboration between us and interchange ideas\, ISC – 2024 will include different poster sessions. Therefore\, we would like to invite you to submit an abstract to the school. The abstract and poster are intended to promote the interchange of ideas between us\, students and teachers. See further instruction https://www.hypertools.org/isc \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2024-dl-deep-learning/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240528
DTEND;VALUE=DATE:20240531
DTSTAMP:20260414T211714
CREATED:20240424T084527Z
LAST-MODIFIED:20240424T084527Z
UID:10001178-1716854400-1717113599@ddsa.dk
SUMMARY:Confronting Data through Design Methods – Speculating with Generative AI (GAI)
DESCRIPTION:Content \nThis course is aimed at PhD students\, researching within the fields of Computer-Supported Cooperative Work (CSCW)\, Human-Computer Interaction (HCI)\, Science- and Technology Studies (STS)\, Participatory Design (PD) & Critical Data Studies\, but the course is open to PhD students from all areas of work- and design studies. The course is given as a mix of hands-on exercises with GenAI tools and lectures and seminars on speculative design and critical responses to GenAI interwoven throughout the 3-day course. In addition\, the students engage in peer-feedback as part of the development of their essays\, which focus on applying GenAI in relation to their own PhD project. \nThe course explores how we can use design methods to probe\, construct\, question\, and critique different types of data. The goal of the course is that participants are introduced to both theoretical\, concrete\, and practical knowledge about different modes of doing research through design as well as gaining an overview of current debates regarding how data-driven technologies can be made ethical and responsible. \nThis year’s course focus on applying GenAI for data analysis within this area of research. \nThe rapid introduction of GAI into organizational work through formal digital transformation initiatives as well as informal adoption of freely available tools is quickly reconfiguring the conditions of collaborative organizing and the means through which we speculate futures labor and society. How do we approach\, for example\, which practices and skills we automate or retain as requiring human experience?  What futures are rendered more realizable through AI-enhanced data analysis methods and techniques? How is this moment of GAI hype and increased accessibility impacting forms of expertise\, authority\, and accountability in data work?   \nWhile GAI is entering data work for its expediency and utility\, it is not always held accountable as a method of speculation and design even as it shapes the methods and tools through which we develop future scenarios with and through data analysis. Adopting a design perspective\, we will also attend to the people in each case who are the subjects of data and have a stake in design outcomes of working with large-scale data\, accessible for them with GAI. \nParticipants will obtain concrete skills in designing participatory “scenario-based workshops” utilizing GenAI tools\, including DALL-E and ChatGPT. Furthermore\, the course is set up to facilitate discussions and to generate ideas relating to the participants own PhD projects. \nWorking hands-on with GAI in a speculative design and research through design approach\, will enable participants to enter into debates over responsible use of AI and other data-driven technologies through concrete application of these tools. By applying speculative methods to consider future scenarios of organizing and collaborative work students will problematize and concretize opportunities for designing/using data-driven technologies ethically and responsibly in their own cases. \nThe course is offered as a collaboration between DIREC\, ITU and UCPH.   \nAccessibilityIf any participants have any special needs in order to attend the course\, they are kindly requested to contact the organizers and we will try to accommodate such needs. \nPreparation:In order to prepare for the course\, the course participants need to: \n(1) Read the literature from the reading list prior to the course (the course curriculum will be distributed after enrollment in the course). Download free version of DALL-E and ChatGPT. \n(2) Submit their essays before May 15 2024 (2-4 pages) reflecting on the question: “How might combining methods from speculative design and GenAI help you think about your data in new ways?  \nThe readings and the essays are a way to reflect upon the topics prior to the course. The essays will also help us to identify participants interests/considerations prior to the course. Furthermore\, this preparatory work aims to support their active participation throughout the course. \nLearning outcome \nKnowledge:• Knowledge of methods for speculative design• Knowledge of GenAI tools\, limitations and possibilities \nSkills:• Skill 1\, application of GenAI tools for research (DALL-E\, ChatGPT)• Skill 2\, design of scenario-based workshops with GenAI \nCompetences:• Competence 1\, Reflect on prompting with GenAI tools for the purpose of speculative design.• Competence 2\, Design participatory scenario workshops with GenAI tools. \nLecturers \nNaja Holten Møller is Associate Professor at the University of Copenhagen (UCPH)\, Department of Computer Science and founder and organizer of the Confronting Data Co-lab (www.confrontingdata.dk). Her research explores how data-driven technologies introduce continuous forms of change for bureaucracies and public decision-\, but also for citizens and others who engage with these work processes. She was part of the expert group initiated by the Danish Ministry of Digitalization and public- and private partners that analyzed possibilities and challenges of using language models in the Danish public sector. \nMarisa Leavitt Cohn is an Associate Professor in the Technologies in Practice Research Group at the IT University of Copenhagen where she directs ETHOS (ethos.itu.dk)\, a feminist methods lab working with emergent intersections of ethnographic and digital methods to explore computational culture and the politics of data. Cohn’s research is positioned within Science and Technology Studies (STS)\, Software Studies\, Human Computer Interaction. She applies ethnography and research-through-design to examine software maintenance labor\, infrastructural decay\, and temporal politics of technological change. She is particularly interested in how temporal narratives of evolvability\, emergence\, decline\, and obsolescence shape co-speculative approaches to designing computational futures. \nChristopher Le Dantec is Full Professor at Northeastern University’s with a joint appointment at Computer Sciences and College of Art\, Media\, and Design. His research is focused on digital civics\, an area of innovation that emerges at the intersection of Computer Science\, Participatory Design\, Digital Democracy\, and Smart Cities. Together with students and community partners\, he works to co-create new interactive digital technologies that assert identity\, that enable new forms of civic participation through environmental sensing\, and that respect community experiences\, beliefs\, and desires. \nChristian Villum (https://autofunk.dk/about/) is an independent researcher at Autofunk. Driven by a keen interest in examining new boundaries for the application of digital technology\, his work has two trajectories. On the one hand\, exploring new technological currents and their impact on society\, and on the other hand pushing the boundaries of digital art and culture\, in particular in the electronic music realm. Recently\, Villum has taken on GenAI as a medium for how he engages with the design field to co-create scenarios for imagining alternative future(s). \nMark Friis Hau  is a postdoctoral researcher at the Department of Sociology\, Research Center for Industrial Relations at the University of Copenhagen\, where he leads the network at KU exploring the possibilities of using GenAI in teaching. As part of this work\, he has developed and tested a “Socratic bot” that can ask guiding questions\, and is experimenting with new ways to support students learning – but also people in general – by strengthening their “prompting” skills. \nThomas T Hildebrandt is Full Professor at the University of Copenhagen (UCPH)\, Department of Computer Science and an active participant in DIREC – the Digital Research Center Denmark. He engages in the public discussion of GenAI from the perspective of software engineering and how we can ensure reliable and flexible software systems that are suitable for the people who use them\, including the digitization of legal\, workflow and business process information systems. \nRemarks \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/confronting-data-through-design-methods-speculating-with-generative-ai-gai/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240527
DTEND;VALUE=DATE:20240601
DTSTAMP:20260414T211714
CREATED:20240424T090341Z
LAST-MODIFIED:20240424T090409Z
UID:10001155-1716768000-1717199999@ddsa.dk
SUMMARY:International School of Chemometrics 2024 - INTERMEDIATE - Intermediate Topics on Chemometrics. DoE\, Variable Selection Methods\, Multivariate Curve Resolution
DESCRIPTION:Aim and content \nThe “International School of Chemometrics (ISC) – 2024” is a four-week PhD school specifically aimed at people having acquired some basic understanding of chemometrics. ISC will be offered at practitioner level. \n“ISC-2024” is addressed to PhD students/post-docs\, associate professors\, etc. who want to acquire further knowledge on advanced multivariate data analysis from different disciplines (Chemistry\, Physics\, Food Science\, Biology\, Geology\, Environmental Sciences\, etc.). ISC also addresses companies or research laboratories who want to implement advanced multivariate data analysis in their daily research environment. \nISC aims at being a platform for: \n– Learning data analysis methods. ISC is specifically designed for researchers who want to acquire extra knowledge on multivariate data analysis and adapt it in their routine work. \n– Sharing knowledge and interchange of ideas between students covering different scientific backgrounds. One of the key points of the course is the interaction between the students and troubleshooting – always within the framework of scientific data analysis and performance. \n– Meeting world-wide recognized experts of Multivariate Data Analysis in an open discussion forum environment. ISC will count with teachers that are well-recognized experts on chemometrics and multivariate data analysis in their respective fields. \nThis\, at the same time\, will offer the possibility of opening new collaborative frameworks between students and teachers. The students enrolling will have the opportunity of choosing the seminars that they consider most relevant\, without the obligation of attending a minimum amount of seminars. \nSeminars and dates:Each seminar is independent and the registration is individual. The students can choose to attend the seminars which they consider more relevant for their research. There is no minimum of seminars that the student must attend. \n1 – PROGRAMMING (José Manuel Amigo\, Sergey Kucheryavskyi and Anders Krogh Mortensen): This online prerecorded videos are thought to be an introduction of the main aspects dealing with Matlab\, R and Python. Check the “detailed information website” for more information. 1 ECTS. Start the 13th of May\, 2024. \n2 – BASIC (José Manuel Amigo and Morten Rasmussen): This seminar includes 2 parts. EXPLORE and LINAL. Basic introduction to Chemometrics. Data types\, PCA\, pre-processing\, Multivariate Regression\, Linear Algebra for Multivariate Data Analysis. 5 days. 2.5 ECTS. 20 – 24 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n3 – INTERMEDIATE (Agnieszka Smolinska\, Asmund Rinnan\, Anna de Juan): This seminar includes 3 parts. DoE (Design of Experiments)\, VARSEL (variable selection methods) and MCR (Multivariate Curve Resolution). 5 days. 2.5 ECTS. 27 – 31 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n4 – DEEP LEARNING (Jesper Løve Hinrich): This seminar is an introduction to non-linear methods and deep learning. 3 days. 1.5 ECTS. 3 – 5 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n5 – CLASS (Davide Ballabio): The course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine\, Random Forests and Self-Organized Maps Artificial Neural Networks. 4 days. 2 ECTS. 3 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n6 – DATA FUSION (Federico Marini): Data Fusion is gaining extreme importance in science. This seminar is an introduction to the main methods to fuse your data. 2 days. 1 ECTS. 5 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n7 – MULTIWAY (Beatriz Quintanilla): Methods like PARAFAC\, PARAFAC2 and N-PLS will be taught. 1 day. 0.5 ECTS. 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n8 – GLUE (José Manuel Amigo\, Rasmus Bro\, Federico Marini\, Davide Ballabio): How NOT to Make Chemometrics and afternoon workshop to talk about your particular needs. 1 day. NO ECTS. 7 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \nExtremely important note: The last week\, the seminars are simultaneous. Therefore:– Attending DL means that you cannot sign up for CLASS and DF.– Attending CLASS means that you cannot sign up for DL and DF.– Attending DF means that you cannot sign up for DL\, CLASS and MW.– Attending MW means that you cannot sign up for CLASS and DF.Despite this fact\, attending any of the seminars of the last week will grant you access to the material of all the seminars of that week. \nFurther information:https://www.hypertools.org/isce-mail of ISC (ischemometricshelp@gmail.com) \nINTERMEDIATE – Intermediate topics on Chemometrics. DoE\, Variable selection methods\, Multivariate Curve Resolution \nThis seminar contains three general topics: \n– DoE – Design of Experiments: The course gives an introduction to the Design of Experiments. The course will highlight the critical points to address when designing our experiments. Some classical designs will be discussed (Full Factorial\, Plackett-Burman\, Central Composite) together with more advanced approaches like D-Optimal Designs. \n– VARSEL – Variable selection methods: In this one-day hands-on course on variable selection\, you will become familiar with state-of-the-art variable selection. This will cover both classical\, iterative\, model-based and nature-inspired algorithms. I will also give you some pros and cons of the different methods\, and some suggestions for how to operate them even better. The cases you will be working with can be done in R\, Matlab (w/ wo PLS-toolbox) or python\, all as you see fit. However\, I only provide the necessary toolboxes for Matlab. \n– MCR – Multivariate Curve Resolution: The module will address the theoretical description and hands-on application of Multivariate Curve Resolution (MCR). MCR is a multivariate resolution (unmixing) method that can provide the description of a multicomponent data set through a bilinear model of chemically meaningful profiles\, e.g.\, when analyzing an HPLC-DAD data set\, MCR would provide the real elution profiles and the related UV spectra for each compound in the sample. It has applications in diverse fields\, such as process analysis\, chromatographic data\, hyperspectral images or environmental data\, in any context where a mixture analysis problem can be encountered. MCR can be applied to a single data matrix or to multiset structures formed by blocks of different information (data fusion). The module focuses mainly on the algorithm MCR-ALS (Multivariate Curve Resolution-Alternating Least Squares)\, and hands-on work will be done using a dedicated free GUI interface adapted to MATLAB environment. Applications will cover many of the areas mentioned above. \nDates and timetable: 27th May – 31st of May. From 9 am until 4 pm (CET) with 1-hour lunch break (30 hours) \nPrevious knowledge needed: Basic multivariate data analysis and Matlab \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with:– Matlab– PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO\, a fully functional demo will be available for the School.– MCR-ALS toolbox: MCR-ALS toolbox can be freely downloaded here: https://mcrals.wordpress.com/download/mcr-als-2-0-toolbox/– R Studio \nTeachers: Agnieszka Smolinska (DoE)\, Asmund Rinnan (VARSEL)\, Anna de Juan (MCR). \nLearning outcome \nThe intended learning outcomes are divided into two categories: \n1) Individual learning outcomes: the main target for each individual seminar is to learn the basis of one data analysis method focused on several proposed examples. The students have to be able to apply the acquired knowledge to any problem related to their own research. \n2) Global learning outcomes: Students attending all the seminars will\, at the end of the course\, be able to understand the structure of a vast amount of data structures and also to understand the problems derived from the data. Moreover\, they will be independent in the application of solutions to their problems in a dedicated manner. \nRemarks \nThe price for participating is as follows (same price for online and physically present): \nAcademia: DKK 600 per ECTS (approx. EUR 81 / USD 98)Industry: DKK 1500 per ECTS (aprox. EUR 200 / USD 245) \nPayment must be completed before the course starts. Information on the method of payment will be provided after confirmation of registration. \nAll seminars include all the material that the student might need:– Slides of the course (pdf).– Exercises– Datasets– Toolboxes– Refreshment during the lessons (coffee\, tea\, candies\, cookies\, and other amenities)– We do NOT provide: Matlab and lunch \nBest Poster Award for physically present attendants:In order to foment collaboration between us and interchange ideas\, ISC – 2024 will include different poster sessions. Therefore\, we would like to invite you to submit an abstract to the school. The abstract and poster are intended to promote the interchange of ideas between us\, students and teachers. See further instruction https://www.hypertools.org/isc \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2024-intermediate-intermediate-topics-on-chemometrics-doe-variable-selection-methods-multivariate-curve-resolution/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240520
DTEND;VALUE=DATE:20240525
DTSTAMP:20260414T211714
CREATED:20240424T082648Z
LAST-MODIFIED:20240424T082648Z
UID:10001154-1716163200-1716595199@ddsa.dk
SUMMARY:International School of Chemometrics 2024 - BASIC - Basic Introduction to Chemometrics\, Data Types\, Data Pre-Processing\, PCA\, Multivariate Linear Regression and Linear Algebra for Multivariate Data Analysis
DESCRIPTION:Aim and content \nThe “International School of Chemometrics (ISC) – 2024” is a four-week PhD school specifically aimed at people having acquired some basic understanding of chemometrics. ISC will be offered at practitioner level. \n“ISC-2024” is addressed to PhD students/post-docs\, associate professors\, etc. who want to acquire further knowledge on advanced multivariate data analysis from different disciplines (Chemistry\, Physics\, Food Science\, Biology\, Geology\, Environmental Sciences\, etc.). ISC also addresses companies or research laboratories who want to implement advanced multivariate data analysis in their daily research environment. \nISC aims at being a platform for: \n– Learning data analysis methods. ISC is specifically designed for researchers who want to acquire extra knowledge on multivariate data analysis and adapt it in their routine work. \n– Sharing knowledge and interchange of ideas between students covering different scientific backgrounds. One of the key points of the course is the interaction between the students and troubleshooting – always within the framework of scientific data analysis and performance. \n– Meeting world-wide recognized experts of Multivariate Data Analysis in an open discussion forum environment. ISC will count with teachers that are well-recognized experts on chemometrics and multivariate data analysis in their respective fields. \nThis\, at the same time\, will offer the possibility of opening new collaborative frameworks between students and teachers. The students enrolling will have the opportunity of choosing the seminars that they consider most relevant\, without the obligation of attending a minimum amount of seminars. \nSeminars and dates:Each seminar is independent and the registration is individual. The students can choose to attend the seminars which they consider more relevant for their research. There is no minimum of seminars that the student must attend. \n1 – PROGRAMMING (José Manuel Amigo\, Sergey Kucheryavskyi and Anders Krogh Mortensen): This online prerecorded videos are thought to be an introduction of the main aspects dealing with Matlab\, R and Python. Check the “detailed information website” for more information. 1 ECTS. Start the 13th of May\, 2024. \n2 – BASIC (José Manuel Amigo and Morten Rasmussen): This seminar includes 2 parts. EXPLORE and LINAL. Basic introduction to Chemometrics. Data types\, PCA\, pre-processing\, Multivariate Regression\, Linear Algebra for Multivariate Data Analysis. 5 days. 2.5 ECTS. 20 – 24 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n3 – INTERMEDIATE (Agnieszka Smolinska\, Asmund Rinnan\, Anna de Juan): This seminar includes 3 parts. DoE (Design of Experiments)\, VARSEL (variable selection methods) and MCR (Multivariate Curve Resolution). 5 days. 2.5 ECTS. 27 – 31 May 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n4 – DEEP LEARNING (Jesper Løve Hinrich): This seminar is an introduction to non-linear methods and deep learning. 3 days. 1.5 ECTS. 3 – 5 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n5 – CLASS (Davide Ballabio): The course will deal with the main linear classification methods like Discriminant Analysis\, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine\, Random Forests and Self-Organized Maps Artificial Neural Networks. 4 days. 2 ECTS. 3 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n6 – DATA FUSION (Federico Marini): Data Fusion is gaining extreme importance in science. This seminar is an introduction to the main methods to fuse your data. 2 days. 1 ECTS. 5 – 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n7 – MULTIWAY (Beatriz Quintanilla): Methods like PARAFAC\, PARAFAC2 and N-PLS will be taught. 1 day. 0.5 ECTS. 6 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \n8 – GLUE (José Manuel Amigo\, Rasmus Bro\, Federico Marini\, Davide Ballabio): How NOT to Make Chemometrics and afternoon workshop to talk about your particular needs. 1 day. NO ECTS. 7 June 2024. From 9 am until 4 pm\, with 1 hour lunch break. \nExtremely important note: The last week\, the seminars are simultaneous. Therefore:– Attending DL means that you cannot sign up for CLASS and DF.– Attending CLASS means that you cannot sign up for DL and DF.– Attending DF means that you cannot sign up for DL\, CLASS and MW.– Attending MW means that you cannot sign up for CLASS and DF.Despite this fact\, attending any of the seminars of the last week will grant you access to the material of all the seminars of that week. \nFurther information:https://www.hypertools.org/isce-mail of ISC (ischemometricshelp@gmail.com) \nBASIC – Basic introduction to Chemometrics\, data types\, data pre-processing\, PCA\, Multivariate Linear Regression and Linear Algebra for Multivariate Data Analysis \nThis seminar contains two general topics: \n– EXPLORE – Data exploration and regression: Principal Component Analysis has become the most powerful and versatile tool for exploring data tables in Analytical Sciences. Here we present a course to show the main benefits and drawbacks of PCA when it is used for different kinds of analytical data: Spectroscopy\, environmental assessment\, sensory\, experiments performance\, chromatography\, etc. Moreover\, preprocessing of different types of data will also be addressed in the seminar as a prerequisite for having the optimal possibility for exploring the data. If PCA is the keystone of pattern recognition methods\, PLS is the keystone of multivariate calibration methods. This seminar will give a general overview of different multivariate calibration strategies (Multilinear Regression\, Principal Component Regression)\, and will focus on Partial Least Squares regression. \n– LINAL – Linear Algebra: The Foundation for chemometric modeling is Linear Algebra. Why do the algorithms work? Why are the models meaningful? A math-derived answer to these questions can be found using linear algebra. The seminar will focus on hands-on experience with some fundamental linear algebra concepts\, including rank\, determinant\, inverse\, pseudo inverse\, eigenvalues\, singular value decomposition\, orthogonality and basis sets. We will analyze a few real-life datasets\, but the purpose of the seminar is to be a proficient mechanic unraveling the black box of algorithms and models\, while other courses will learn you how to drive the car.  \nDates and timetable: 20th of May – 24th of May. From 9 am until 4 pm (CET) with a 1-hour lunch break (30 hours) \nPrevious knowledge needed: None \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with: \n– Matlab– PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO\, a fully functional demo will be available for the School. \nTeachers: José Manuel Amigo (EXPLORE) and Morten A. Rasmussen (LINAL). \nLearning outcome \nThe intended learning outcomes are divided into two categories: \n1) Individual learning outcomes: the main target for each individual seminar is to learn the basis of one data analysis method focused on several proposed examples. The students have to be able to apply the acquired knowledge to any problem related to their own research. \n2) Global learning outcomes: Students attending all the seminars will\, at the end of the course\, be able to understand the structure of a vast amount of data structures and also to understand the problems derived from the data. Moreover\, they will be independent in the application of solutions to their problems in a dedicated manner. \nRemarks \nThe price for participating is as follows (same price for online and physically present): \nAcademia: DKK 600 per ECTS (approx. EUR 81 / USD 98)Industry: DKK 1500 per ECTS (aprox. EUR 200 / USD 245) \nPayment must be completed before the course starts. Information on the method of payment will be provided after confirmation of registration. \nAll seminars include all the material that the student might need:– Slides of the course (pdf).– Exercises– Datasets– Toolboxes– Refreshment during the lessons (coffee\, tea\, candies\, cookies\, and other amenities)– We do NOT provide: Matlab and lunch \nBest Poster Award for physically present attendants:In order to foment collaboration between us and interchange ideas\, ISC – 2024 will include different poster sessions. Therefore\, we would like to invite you to submit an abstract to the school. The abstract and poster are intended to promote the interchange of ideas between us\, students and teachers. See further instruction https://www.hypertools.org/isc \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-2024-basic-basic-introduction-to-chemometrics-data-types-data-pre-processing-pca-multivariate-linear-regression-and-linear-algebra-for-multivariate-data-an/
CATEGORIES:PhD Course
END:VEVENT
END:VCALENDAR