Year 1
Programme overview for year 2025/2026
The programme overview is adopted 2025-02-20 by Dean of Education.
AUTUMN TERM
Study period 1
Compulsory courses
Course code and nameModule, credits/periodBlockNoteExaminationRe-examination (okt 2025 - aug 2026)FFR105 Stochastic optimization algorithms Examination 7.5 credits E FFR135 Artificial neural networks Examination 7.5 credits E Elective courses
Course code and nameModule, credits/periodBlockNoteExaminationRe-examination (okt 2025 - aug 2026)EEN100 Statistics and machine learning in high dimensions Oral examination 6 credits 1), 2) EEN100 Statistics and machine learning in high dimensions Project 1.5 credits S, 1), 2) KMG060 Systems biology Examination 7.5 credits S, 1), 2) RRY025 Image processing Examination 7.5 credits E, 1), 2) TIF160 Humanoid robotics Project 7.5 credits E, 1), 2) TIF295 Experimental methods in modern physics Project 3 credits E, 1), 2) TIF430 Quantum mechanics Oral examination 4.5 credits E, 1), 2) TIN093 Algorithms Examination 7.5 credits E, 1), 2) TMA882 High performance computing Examination 7.5 credits E, 1), 2) TMA947 Nonlinear optimisation Laboratory 1.5 credits 1), 2) TMA947 Nonlinear optimisation Examination 6 credits S, 1), 2) TMS165 Stochastic calculus Examination 7.5 credits E, 1), 2)
Study period 2
Compulsory courses
Course code and nameModule, credits/periodBlockNoteExaminationRe-examination (okt 2025 - aug 2026)FFR120 Simulation of complex systems Project 7.5 credits E TIF155 Dynamical systems Examination 7.5 credits E Elective courses
Course code and nameModule, credits/periodBlockNoteExaminationRe-examination (okt 2025 - aug 2026)ENM140 Game theory and rationality Project 7.5 credits E, 1), 2) FKA122 Computational physics Project 7.5 credits E, 1), 2) MTF073 Computational fluid dynamics: The finite volume method (CFD) Written and oral assignments (part A) 1.5 credits 1), 2) MTF073 Computational fluid dynamics: The finite volume method (CFD) Written and oral assignments (part B) 1.5 credits 1), 2) MTF073 Computational fluid dynamics: The finite volume method (CFD) Written and oral assignments (part C) 1.5 credits 1), 2) MTF073 Computational fluid dynamics: The finite volume method (CFD) Examination 3 credits S, 1), 2) MVE095 Options and mathematics Examination 7.5 credits E, 1), 2) TDA251 Algorithms, advanced course Project 7.5 credits E, 1), 2) TDA507 Computational methods in bioinformatics Written and oral assignments 7.5 credits E, 1), 2) TIF181 Science, innovation and entrepreneurship Project 7.5 credits E, 1), 2)
SPRING TERM
Study period 3
Compulsory courses
Course code and nameModule, credits/periodBlockNoteExaminationRe-examination (okt 2025 - aug 2026)FFR110 Computational biology Examination 7.5 credits E Elective courses
Course code and nameModule, credits/periodBlockNoteExaminationRe-examination (okt 2025 - aug 2026)DAT675 Artificial intelligence for molecules Written and oral assignments 3 credits DAT675 Artificial intelligence for molecules Project 4.5 credits S MVE155 Statistical inference Examination 7.5 credits E, 1) MVE220 Financial risk Examination 7.5 credits E TDA206 Discrete optimization Examination 7.5 credits E TDA233 Algorithms for machine learning and inference Written and oral assignments 3 credits TDA233 Algorithms for machine learning and inference Examination 4.5 credits S TIF150 Information theory for complex systems Examination 7.5 credits E, 1) TIF320 Computational materials and molecular physics Project 7.5 credits E, 1) TMA285 Financial derivatives and partial differential equations Examination 7.5 credits E, 1) TME286 Interpretable artificial intelligence Project 7.5 credits E, 1)
Study period 4
Elective courses
Course code and nameModule, credits/periodBlockNoteExaminationRe-examination (okt 2025 - aug 2026)MTF271 Turbulence modeling Written and oral assignments (part A) 1.5 credits 1) MTF271 Turbulence modeling Written and oral assignments (part B) 1.5 credits 1) MTF271 Turbulence modeling Examination 4.5 credits S, 1) MVE166 Linear and integer optimization with applications Examination (part A) 6 credits 1) MVE166 Linear and integer optimization with applications Project (part B) 1.5 credits S, 1) MVE441 Statistical learning for big data Project 1.5 credits 1) MVE441 Statistical learning for big data Take-home examination 6 credits S, 1) TIF106 Non-equilibrium processes in physics, chemistry and biology Examination 7.5 credits E, 1) TIF360 Advanced machine learning with neural networks Project 7.5 credits E, 1) TME290 Autonomous robots Examination 7.5 credits E, 1) TMS016 Spatial statistics and image analysis Examination 7.5 credits S, 1) TMS088 Financial time series Examination 7.5 credits E, 1)
- 1 Compulsory elective: - (EEN100, ENM140, FKA122, KMG060, MTF073, MTF271, MVE095, MVE155, MVE166, MVE441, RRY025, TDA251, TDA507, TIF106, TIF150, TIF160, TIF181, TIF295, TIF320, TIF360, TIF430, TIN093, TMA285, TMA882, TMA947, TME286, TME290, TMS016, TMS088, TMS165) 22.5 credits of stated courses are required for the degree
- 2 Recommendation: The course is normally followed during the second year of the Masters programme (EEN100, ENM140, FKA122, KMG060, MTF073, MVE095, RRY025, TDA251, TDA507, TIF160, TIF181, TIF295, TIF430, TIN093, TMA882, TMA947, TMS165)
- DIG: Digital examination is an examination written in the Inspera system. The student will bring their own computer and access the exam via Safe exam browser.
- E: The only module in the course. Module grade and grade for the course are reported at the same time.
- S: Final grade. All module grades are reported before the final grade for the course can be reported.