Programme structure

On this page you can find detailed information about the programme’s content.

Outline

The structure of the programme is flexible in order to increase your opportunities to tailor your studies to your own interests, which means that some courses may be offered across several different semesters. As a result, the course list below does not always provide a complete picture of the semester in which you will study a specific course.

During your studies you will take 22.5 credits elective courses, which must include two of the following courses:

NUMN21
NUMN26
NUMN28
NUMN33
BERN04
BERN07
BERN09

and one of the following courses:

MASC13
MASM15
MASM17
MASM25
MASM26
MASM27
BERN02

In addition, you will take 37.5 credits elective courses, which must include 22.5 credits in computational science, mathematics, numerical analysis, and mathematical statistics and 15 credits in another natural science subject or computer science.

Course overview

Year 1

Name	Type	Semester	Credits
Computational Science: Modelling in Computational Science	Compulsory	1	7.5
Numerical Analysis: Numerical Methods for Differential Equations	Compulsory	1	7.5
Mathematical Statistics: Stationary Stochastic Processes	Compulsory	1	7.5
Computational Science: Introduction to Artificial Neural Networks and Deep Learning	—	1	7.5
Mathematical Statistics: Monte Carlo Methods for Statistical Inference	Compulsory	2	7.5
Numerical Analysis: Simulation Tools	—	2	7.5
Numerical Analysis: Numerical Simulations of Flow Problems	—	2	7.5
Numerical Analysis: Numerical Methods for Partial Differential Equations	—	2	7.5
Computational Science: Uncertainty Quantification & Data-driven Modelling	—	2	7.5
Mathematical Statistics: Statistical Modelling of Extreme Values	—	2	7.5
Mathematical Statistics: Stationary and Non-stationary Spectral Analysis	—	2	7.5
Mathematical Statistics: Nonparametric Inference	—	2	7.5

Year 2

Name	Type	Semester	Credits
Numerical Analysis: Advanced Course in Numerical Algorithms with Python/SciPy	—	3	7.5
Computational Science: Parallel Programming in Scientific Computing	—	3	7.5
Mathematical Statistics: Markov Processes	—	3	7.5
Mathematical Statistics: Time Series Analysis	—	3	7.5
Mathematical Statistics: Spatial Statistics with Image Analysis	—	3	7.5
Computational Science: Reproducible Data Science and Statistical Learning	—	3	7.5
Computational Science - Scientific Computing: Master's Degree Project	Compulsory	4	30

Programme syllabus

The syllabus provides an overview of the programme. It outlines the admission requirements, lists the courses included, and explains which degree the studies lead to.

Syllabus (PDF, new tab)