Engineering mathematics and computational science, MSc

120 credits (2 years)

Sign up for informationGain a solid foundation in mathematics and the skills to formulate and solve many of the problems that are posed by industry, business and research.​

Engineering mathematics and computational science​ master's programme at Chalmers

Nowadays, one can without hesitation, state that there is mathematics in virtually everything. There is mathematics behind mobile phones, cars and oil tankers. Internet is built on mathematics and Google is based on probability theory. Cryptology and secure data transfer are based on classical number theory. New mathematical methods are necessary for understanding modern physics and chemistry, e.g. advanced mathematical and statistical models are required in climate science.
Bioinformatics has quickly grown to become one of the big areas of applied mathematics. Continuing the development of mathematical methods is necessary in order to deal with the huge amounts of information generated by e.g. gene mapping.

The master's programme provides a solid base in mathematics and/or mathematical statistics or computational science. It is also possible to choose a direction towards bioinformatics. We also offer a number of courses in financial mathematics.

On completion of the master's programme, you will be able to not only master a given area of engineering but also take part in the development of mathematical models and algorithms.
Engineering mathematics and computational science master's programme turns to you who wants to sharpen your engineering studies with state-of-the-art mathematical competence as well as to you who with a keen curiosity-driven mathematical interest. Of course, it also turns to you who have realised that deep mathematical knowledge is on-demand in an ever-increasing number of areas.

Topics covered

The subjects of mathematical statistics, computational science and engineering mathematical modelling are fundamental areas in the Engineering Mathematics and Computational Science master’s programme. The courses included in the programme plan handle topics such as engineering mathematics, applied mathematics, mathematical biology, machine learning, big data science, pure math and mathematical physics.

Master's programme structure

The master's programme runs for a duration of two years, leading to a Master of Science (MSc) degree. During each year, students can earn 60 credits (ECTS) and complete the programme by accumulating a total of 120 credits. Credits are earned by completing courses where each course is usually 7.5 credits. The programme consists of Compulsory courses, Compulsory elective courses and Elective courses.

Compulsory courses year 1

During the first year of the programme, there are four compulsory courses that form a common foundation in Engineering Mathematics and Computational Science. Each course is 7.5 credits.
  • High-performance computing
  • Nonlinear optimization
  • Statistical inference
  • Partial differential equations (can be substituted for a compulsory elective course if student has taken a similar course previously)​

Compulsory courses year 2

During the second year, there is one compulsory course  (Project course in mathematical and statistical modelling).
In addition, in order to graduate, a master's thesis must be completed. The thesis may be worth 30 credits or 60 credits depending on your choice.

Compulsory elective courses *profile, or elective course (choose 3 or more)

Through compulsory elective courses, you can then specialize in one of the following profiles. The profiles provided are mere suggestions. It is not required to adhere to these profiles. Students are encouraged to create their own customized, individual profiles based on their particular interests and motivation.​

Profile: Mathematical biology

  • Intro to bioinformatics
  • Computational biology
  • Systems biology
  • Linear statistical models

Profile: ​Financial mathematics

  • Options
  • Financial derivatives and PDE
  • Financial time series
  • Stochastic calculus​

Profile: Machine learning

  • Algorithms
  • Large scale optimization
  • Artificial neural networks
  • Stochastic optimization algorithms

Profile: Big data science

  • ​Linear statistical models​
  • Large scale optimization
  • Statistical learning for big data
  • Artificial neural networks​

Profile: Engineering

  • PDEs
  • Mechanics of solids
  • Mechanics of fluids
  • Computational fluid dynamics​

Profile: Pure math and mathematical physics

  • Algebra
  • Integration theory
  • Functional analysis
  • Foundations of probability theory


Roughly half of the previous students from this master's programme have stayed in academia as PhD students, in Sweden as well as abroad. ​Others have become employed in industry, for example in the automotive industry, the pharmaceutical industry, the telecom industry, the IT and software industry, the banking and insurance industry or as consultants for the industry. 

There is a broad range of companies where former students now are employed. Some examples are Ericsson, Volvo, Astra Zeneca, RUAG, Andra AP-fonden, Google, Uber and Jeppesen. 

Research within Engineering mathematics and computational science​​

Research in mathematical sciences, in Sweden and elsewhere, is very intense. Mathematical research is in demand from an increasing number of sectors (for instance within artificial intelligence and machine learning) and at the same time, mathematics continues to flourish as research in its own right. 
These directions of research are not in opposition to each other. On the contrary, they enrich and inspire each other. It is a fact that many of the mathematical results that are of very concrete use today, were developed by pure mathematical curiosity, sometimes decades or even centuries ago. 
Swedish mathematical research is by tradition strong. This is true also for the Department of Mathematical Sciences at Chalmers and Gothenburg University. The Department is joint for the two universities, which helps to give it the size needed to create a stimulating environment, socially and scientifically. For more information about the research at the department, follow the links below.   


​Sustainable development

The programme is highly interlinked with the achievement of the UN Sustainable development goals (SDGs)​. The table below provides an overview of the sustainable development goals and the associated targets within the programme.
SDGs for Engineering mathematics at Chalmers

Goal 3: Good health and well-being
Effective and efficient healthcare is essential to our well-being. Mathematics in combination with computational science is a major driving force in the development of new medical treatments and diagnostics that will be essential to ensuring well-being for all.  

Goal 9: Industry, innovation, and infrastructure
Building a resilient infrastructure for the future will require combining sophisticated algorithms including machine learning, together with networks, and computational methods to handle large data sets. These are core competencies that you will acquire in this master’s programme.

Goal 11: Sustainable cities and communities 
Promoting skills for data analysis, predicting expected outcomes, and supporting decision-making imply that the distribution of resources can be streamlined and the production of toxic waste can be reduced. You will obtain the tools to create smarter and more sustainable manufacturing processes. 

Goal 13: Climate action
This goal requires advanced modelling to understand the threats and the implications of action or lack of action. Courses such as Statistical inference, Computational methods for Bayesian statistics, and Computational Biology give a good understanding of and respect for the complexity of such problems, and the basic skills needed to further develop the relevant science to combat climate change and its impacts. 

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Page manager Published: Mon 17 Oct 2022.