The programme overview is adopted 2026-02-19 by Dean of Education.

• AUTUMN TERM

  • Study period 1

    • Compulsory courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      TMA883 High performance computing	Written and oral assignments 7.5 credits		E
      TMA947 Nonlinear optimisation	Laboratory 1.5 credits
      TMA947 Nonlinear optimisation	Examination 6 credits		S

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      EEN100 Statistics and machine learning in high dimensions	Oral examination 6 credits		1)
      EEN100 Statistics and machine learning in high dimensions	Project 1.5 credits		S, 1)
      FFR105 Stochastic optimization algorithms	Examination 7.5 credits		E, 1)
      FFR135 Artificial neural networks	Examination 7.5 credits		E, 1)
      KMG060 Systems biology	Examination 7.5 credits		S, 1)
      MVE066 Topics in engineering mathematics	Written and oral assignments 7.5 credits		E
      MVE188 Computational methods for Bayesian statistics	Project 2 credits		1)
      MVE188 Computational methods for Bayesian statistics	Examination 5.5 credits		S, 1)
      MVE405 Individual project in mathematics and mathematical statistics	Written and oral assignments 15 credits		E
      TMA265 Numerical linear algebra	Examination 7.5 credits		E, 1)
      TMA401 Functional analysis	Examination 7.5 credits		E, 1)
      TME226 Mechanics of fluids	Written and oral assignments (part A) 1.5 credits		1)
      TME226 Mechanics of fluids	Written and oral assignments (part B) 1.5 credits		1)
      TME226 Mechanics of fluids	Examination 4.5 credits		S, 1)
      TME235 Mechanics of solids	Examination 7.5 credits		E, 1)
      TMS150 Stochastic data processing and simulation	Project 7.5 credits		E, 1)
      TMS165 Stochastic calculus	Examination 7.5 credits		E, 1)
      TMV101 Integration theory	Oral examination 7.5 credits		E, 1)

  • Study period 2

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      DAT450 Machine learning for natural language processing	Written and oral assignments 7.5 credits		S
      EEN020 Computer vision	Project 3 credits
      EEN020 Computer vision	Written and oral assignments 4.5 credits		S
      ENM140 Game theory and rationality	Project 7.5 credits		E, 1)
      MTF073 Computational fluid dynamics: The finite volume method (CFD)	Written and oral assignments (part A) 1.5 credits		1)
      MTF073 Computational fluid dynamics: The finite volume method (CFD)	Written and oral assignments (part B) 1.5 credits		1)
      MTF073 Computational fluid dynamics: The finite volume method (CFD)	Written and oral assignments (part C) 1.5 credits		1)
      MTF073 Computational fluid dynamics: The finite volume method (CFD)	Examination 3 credits		S, 1)
      MVE080 Scientific visualization	Written and oral assignments 7.5 credits		E, 1)
      MVE095 Options and mathematics	Examination 7.5 credits		E, 1)
      MVE140 Foundations of probability theory	Examination 7.5 credits		E, 1)
      MVE170 Basic stochastic processes	Examination 7.5 credits		E, 1)
      MVE190 Statistical learning with regression models	Examination 7.5 credits		E, 1)
      MVE510 Introduction to bioinformatics	Examination 5 credits		1)
      MVE510 Introduction to bioinformatics	Laboratory 2.5 credits		S, 1)
      SJO955 Introduction to law	Examination 7.5 credits		E
      TIF155 Dynamical systems	Examination 7.5 credits		E, 1)
      TMA522 Large scale optimization	Examination (part A) 6 credits		1)
      TMA522 Large scale optimization	Project (part B) 1.5 credits		S, 1)

• SPRING TERM

  • Study period 3

    • Compulsory courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      MVE155 Statistical inference	Examination 7.5 credits		E
      TMA373 Introduction to numerical methods for partial differential equations	Laboratory (part A) 1.5 credits
      TMA373 Introduction to numerical methods for partial differential equations	Examination (part B) 6 credits		S

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      FFR110 Computational biology	Examination 7.5 credits		E, 1)
      MVE150 Algebra	Examination 7.5 credits		E, 1)
      MVE220 Financial risk	Examination 7.5 credits		E
      MVE326 Statistical inference principles	Written and oral assignments (part A) 2.5 credits		2)
      MVE326 Statistical inference principles	Examination (part B) 5 credits		S, 2)
      MVE565 Computational methods for stochastic differential equations	Examination 7.5 credits		E, 1), 2)
      SSY200 Computational electromagnetics	Oral examination 7.5 credits		E, 1)
      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
      TIN093 Algorithms	Examination 7.5 credits		E, 1)
      TMA285 Financial derivatives and stochastic analysis	Examination 7.5 credits		E, 1)
      TMS032 Experimental design and sampling	Examination 7.5 credits		E, 2)

  • Study period 4

    • Elective courses

      Course code and name	Module, credits/period	Block	Note	Examination	Re-examination (okt 2026 - aug 2027)
      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)
      MVE163 Ordinary differential equations and mathematical modelling	Examination 7.5 credits		E, 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)
      MVE330 Stochastic processes	Examination 7.5 credits		E, 2)
      MVE441 Statistical learning for big data	Project 1.5 credits		1)
      MVE441 Statistical learning for big data	Take-home examination 6 credits		S, 1)
      TMA026 Partial differential equations with numerical methods	Examination 7.5 credits		E, 1)
      TMS016 Spatial statistics and image analysis	Examination 7.5 credits		S, 1)
      TMS089 Time series analysis with financial applications	Examination (part A) 6 credits		1)
      TMS089 Time series analysis with financial applications	Project (part B) 1.5 credits		S, 1)

• 1 Compulsory elective: At least three of the courses in brackets must be selected (EEN100, ENM140, FFR105, FFR110, FFR135, KMG060, MTF073, MTF271, MVE080, MVE095, MVE140, MVE150, MVE163, MVE166, MVE170, MVE188, MVE190, MVE441, MVE510, MVE565, SSY200, TIF155, TIN093, TMA026, TMA265, TMA285, TMA401, TMA522, TME226, TME235, TMS016, TMS089, TMS150, TMS165, TMV101) 3 of stated courses are required for the degree
• 2 Is given every other year
• 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.