The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Master Programme in Applied Computational Science, Physical Geography

In the Master Programme in Applied Computational Science with specialisation in Physical Geography, you will learn how to study complex processes in natural sciences, and how Computational Science can contribute to knowledge evolution in society. A few of many examples are environment and climate change, global environmental issues and
global cycles.


The Master Programme in Applied Computational Science with specialisation in Physical Geography will give you detailed knowledge about the underlying methods with respect to physical geography. Applied computational science can be divided into several components: mathematics, modelling, statistics, and programming. The borders are not sharp, as concepts and methods often combine these components.

In addition to knowledge of theory of computational science, there will be an emphasis on obtaining knowledge about the
practical tools that are used by professionals in the field and you will amongst several things train your skills in programming. You will get generic knowledge and skills of importance for computationally intensive jobs such as problem formulation, information search, data processing, scientific writing, and presentation techniques.

Programme structure

Courses 90 credits

NGEN14 Greenhouse Gases and Biogeochemical Cycles, 15 credits
MATA04 Mathematics for Scientists 2, 15 credits
MASB11 Biostatistics - Basic Course 7.5 credits
NUMA01 Computational Programming with Python, 7.5 credits
BERN01 Modelling in Computational Science, 7.5 credits

BERN02 Reproducible Data Science and Statistical Learning, 7.5 credits
BERN03 Introduction to Modelling of Climate Systems 7.5 credits
BERN04 Introduction to Artificial Neural Networks and Deep Learning, 7.5 credits

Degree project 30 credits

BERMXX Degree project Master of Science, 30 credits


Find more information in the programme sheet


Programme coordinator