List of all courses (future and past)
This page lists current and previous COMPUTE courses going back to 2021.
COMPUTE courses 2021 –
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Introduction to computational science, NMG002F (HT26, 3 ECTS)
Dates revised! -
Introduction to Artificial Neural Networks and Deep Learning, NMV012F (HT 2026, 7.5 ECTS)
This course gives an introduction to artificial neural networks and deep learning, both theoretical and practical knowledge. -
Data Literacy with Python, NMA019F (HT 2026, 3 ECTS)
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Parallel Programming of HPC Systems, NTF010F (HT 2026, 7.5 ECTS)
The course discusses programming techniques required to efficiently utilise parallel computing in a computational research project in science or engineering. -
Basic Data Handling and Visualization with R (HT 2026, 1.5 ECTS)
The aim of the course is to provide basic knowledge of the R programming language to facilitate the independent use of tools written and/or implemented in this language, such as… -
Scientific Computing with Python and Fortran, FMA320F (HT 2026, 7.5 ECTS)
This course is intended for students with basic knowledge of programming in any language who would like to learn the techniques of scientific programming. -
Artificial Intelligence in Medicine and Life Science – AI for text and language data (HT 2026, 1.5 and 6 ECTS optio…
This course teaches how deep learning-based models can be used for text and speech analysis and highlights concrete application opportunities and challenges within medicine and… -
Computational Programming with Python - First Steps, NMA017F (Summer 2026, 1.5 ECTS)
The aim of the course is to give an introduction to computational programming in Python for students without any previous programming knowledge. -
Computational Programming with Python, NUMA01F (Summer 2026, 7.5 ECTS)
The aim of the course is to give an introduction to computational programming in Python for students without any previous programming knowledge. -
Low-Rank and Tensor Network Methods for Differential Equations (VT 2026, 4 ECTS)
This course gives PhD students a rigorous and practical introduction to the use of data compression, low-rank matrix methods, and tensor network techniques for the numerical… -
Introduction to R, NMS001F (VT 2026, 1.5 ECTS)
20 - 24 April 2026 -
Agile Software Development, EDAG05F (VT 2026, 7.5 ECTS)
This course will teach knowledge and practical experience of how to develop software together in a team. The focus is on practical experience of methods and tools suitable for a… -
Computational Chemistry, NKE019F (VT 2026, 7.5 ECTS)
Understanding and rationalizing chemical reactions, their mechanism, and the relation between molecular structure and function is key to today's science. It is a difficult… -
Artificial Intelligence in Medicine and Life Science – AI for text and language data (HT 2025, 1.5 and 6 ECTS optio…
This course teaches how deep learning-based natural language processing (NLP) models can be used for text and speech analysis and highlights concrete application opportunities and… -
Basic Data Handling and Visualization with R (HT 2025, 1.5 ECTS)
The aim of the course is to provide basic knowledge of the R programming language to facilitate the independent use of tools written and/or implemented in this language, such as… -
Introduction to Artificial Neural Networks and Deep Learning, NMV012F (HT2025, 7.5 ECTS)
This course gives an introduction to artificial neural networks and deep learning, both theoretical and practical knowledge. -
Artificial Intelligence in Medicine and Life Sciences – Introduction, NTF014F (HT2025, 1.5 ECTS)
This introduction course will give an overview over artificial intelligence concepts and methods and over current and future applications of artificial intelligence in medicine… -
Scientific Computing with Python and Fortran, FMA320F (HT 2025, 7.5 ECTS)
This course is intended for students with basic knowledge of programming in any language who would like to learn the techniques of scientific programming. -
Computational Programming with Python - First Steps (Summer 2025, 1.5 ECTS)
The aim of the course is to give an introduction to computational programming in Python for students without any previous programming knowledge. -
Computational Programming with Python, NUMA01F (Summer 2025, 7.5 ECTS)
The aim of the course is to give an introduction to computational programming in Python for students without any previous programming knowledge. -
Agile Software Development, EDAG05F (VT 2025, 7.5 ECTS)
This course will teach knowledge and practical experience of how to develop software together in a team. The focus is on practical experience of methods and tools suitable for a… -
Data analysis: Statistical learning and visualization, FMSF90F (VT 2025, 7.5 ECTS)
This is a PhD-course in applied statistical learning, i.e. about using statistical techniques, such as modelling and prediction, to analyse real datasets, and making correct… -
Parallel Programming of HPC Systems, NTF010F (HT 2024, 7.5 ECTS)
The course discusses programming techniques required to efficiently utilise parallel computing in a computational research project in science or engineering. -
Introduction to Artificial Neural Networks and Deep Learning, NMV012F (HT2024, 7.5 ECTS)
This course gives an introduction to artificial neural networks and deep learning, both theoretical and practical knowledge. -
Basic Data Handling and Visualization with R (HT 2024, 1.5 ECTS)
The aim of the course is to provide basic knowledge of the R programming language to facilitate the independent use of tools written and/or implemented in this language, such as… -
Scientific Computing with Python and Fortran, FMA320F (HT 2024, 7.5 ECTS)
This course is intended for students with basic knowledge of programming in any language who would like to learn the techniques of scientific programming. -
Computational Programming with Python, NUMA01F (Summer 2024, 7.5 ECTS)
The aim of the course is to give an introduction to computational programming in Python for students without any previous programming knowledge. -
Introduction to R, NMS001F (VT 2024, 1.5 ECTS)
The course covers the basics of R. -
Agile Software Development, EDAG05F (VT 2024, 7.5 ECTS)
This course will teach knowledge and practical experience of how to develop software together in a team. The focus is on practical experience of methods and tools suitable for a… -
Monte Carlo and Molecular Dynamics Tools, NTF008F (VT2024, 7.5 ECTS)
The course provides an introduction to techniques for simulating complex systems and processes with many degrees of freedom, with an emphasis on applications of such techniques. -
Artificial Intelligence in Medicine and Life Sciences – Introduction, NTF014F (HT2023, 1.5 ECTS)
This introduction course will give an overview over artificial intelligence concepts and methods and over current and future applications of artificial intelligence in medicine… -
Introduction to Artificial Neural Networks and Deep Learning, NTF005F (HT2023, 7.5 ECTS)
The overall aim of the course is to give students a basic knowledge of artificial neural networks and deep learning, both theoretical knowledge and how to practically use them for… -
Basic Data Handling and Visualization with R (HT 2023, 1.5 ECTS)
The aim of the course is to provide basic knowledge of the R programming language to facilitate the independent use of tools written and/or implemented in this language, such as… -
Scientific Computing with Python and Fortran, NTF009F (HT2023, 7.5 ECTS)
This course is intended for students with basic knowledge of programming in any language who would like to learn the techniques of scientific programming. -
Computational Programming with Python, NUMA01F (Summer 2023, 7.5 ECTS)
The aim of the course is to give an introduction to computational programming in Python for students without any previous programming knowledge. -
Agile Software Development (VT 2023, 7.5 ECTS)
This course will teach knowledge and practical experience of how to develop software together in a team. -
Data analysis: Statistical learning and visualization, FMSF90F (VT 2023, 7.5 ECTS)
This is a PhD-course in applied statistical learning, ie. about using statistical techniques, such as modelling and prediction, to analyse real datasets, and making correct… -
Computational Chemistry (VT 2023, 7.5 ECTS)
Understanding and rationalizing chemical reactions, their mechanism, and the relation between molecular structure and function is key to today's science. It is a difficult… -
Reproducible and Interactive Data Analysis and Modelling using Jupyter Notebooks, NATF017 (HT2022, study period II,…
The aim of this course is to introduce students to the Jupyter Notebook which is an open-source web application that allows you to create and share documents that contain live… -
Parallel Programming of HPC Systems, NTF010F (HT 2022, 7.5 ECTS)
The course discusses programming techniques required to efficiently utilise parallel computing in a computational research project in science or engineering. -
Advanced Material on Stochastic Numerics, NMA008F (HT 2022)
Stochastic Differential Equations (SDEs) have become a standard tool to model differential equation systems subject to noise. Applications range from Neuroscience or Polymeric… -
Basic Data Handling and Visualization with R (HT2022, 1.5 ECTS)
The purpose of the course is to provide basic knowledge of the programming language R to facilitate independent future use of applications written and / or implemented in this… -
Introduction to Stochastic Numerics (HT2022, 3 ECTS)
Stochastic Differential Equations (SDEs) have become a quite standard tool to model differential equation systems subject to noise. Applications range from Neuroscience or… -
Computational Programming with Python, NUMA01F (HT2022, 7.5 ECTS)
The aim of the course is to give an introduction to computational programming in Python for students without any previous programming knowledge. -
Scientific Computing with Python and Fortran (HT2022, 7.5 ECTS)
This course is intended for students with basic knowledge of programming in any language who would like to learn the techniques of scientific programming. -
Image analysis (VT2022, 7.5 ECTS)
The aim of the course is to give necessary knowledge of digital image analysis for further research within the area and to be able to use digital image analysis within other… -
Introduction to R (VT2022, 1.5 ECTS)
This course aims to provide a basic introduction to R, preparing students for using R in their own research and for additional courses that use R. -
Solving PDEs with DUNE-FEM (VT2022, 3.0 ECTS)
This course is intended for students with intermediate knowledge of programming in Python and basic knowledge of partial differential equations (PDEs) who would like to learn… -
Numerical Analysis of Coupled Problems (HT2021, 7.5 ECTS)
The course discusses numerical methods about coupled problems, where different mathematical models for different physics are coupled along an interface. -
Agile Software Development (HT2021, 7.5 ECTS)
This course will teach knowledge and practical experience of how to develop software together in a team. The focus is on practical experience of methods and tools suitable for a…
Last updated:
13 Feb 2026
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