Complex Adaptive Systems, Master's Programme

Master’s programme
2 years
120 credits (ECTS)
Study pace
Study form
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Application code
Full education cost: 248 000 SEK
First payment: 62 000 SEK

No fees are charged for EU and EEA citizens, Swedish residence permit holders and exchange students.

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The master’s programme in Complex Adaptive Systems addresses fundamental aspects of complex systems in nature and society and links them with an understanding of and skills in using modern algorithms. We focus on using computers and relevant software for simulation and problem solving. As a student of the programme, you will build upon your mathematical knowledge.


How do we understand the dynamics of dust particles in the exhaust of diesel engines, the dynamics of biological or artificial populations, or the problem of teaching a robot how to respond to unexpected changes in its environment? From understanding the fluctuations of share and option prices that determine the stability of our economy, to predicting earthquakes, knowledge and skills in complex adaptive systems are increasingly in demand.

An interdisciplinary and comprehensive education

Interdisciplinary and encompassing several theoretical frameworks, our programme provides you with a broad and thorough introduction to the theory of complex systems and its applications to the world around us. The programme is based on a physics perspective and focuses on its general principles, but we also provide courses in information theory, machine learning, computer science and optimization algorithms, ecology, and genetics in addition to adaptive systems and robotics.
The programme offers three informal tracks, each with a selection of recommended courses:

  • physics/statistical physics
  • robotics and adaptive systems
  • machine learning with applications

Educational methods

Besides traditional lectures on simulation and theory of complex systems, the programme is largely based on numerical calculation and simulation projects and, depending on your elective courses, practical work in the robotics lab. We emphasize problem solving in the form of assignments and smaller projects, where problems are presented that require solutions to be implemented with mathematical software (typically Matlab). One of the mandatory courses (Stochastic Optimization Algorithms) also emphasizes the importance of writing and examining structured program code. In several courses you will work in pairs or small groups. A seminar course spans the entire first year, and functions partly as an introduction, but above all provides training in presentation technology by systematically giving and receiving feedback on presentations. This course also includes an ethics element.

A dynamic and engaging community

The content of the programme is closely connected to the research on machine learning, genetics and turbulence, information theory, and adaptive systems and robotics performed at Chalmers University of Technology and the University of Gothenburg. There is also a lively exchange with international research groups and regular guest lectures on current research that is often directly related to the course material.

Programme structure and content

The first year provides foundational knowledge of complex systems, and provides you the opportunity to design your education according to your own interests. The first semester contains four recommended courses:

  • Neural Networks
  • Stochastic Optimization Algorithms
  • Simulation of Complex Systems
  • Dynamical Systems

The second semester has one recommended course, Computational Biology, and the opportunity to choose elective courses. The following elective courses are recommended:

  • Information Theory of Complex Systems
  • Autonomous robots
  • Intelligent agents
  • Stochastic processes in physics, chemistry and biology
  • Advanced machine learning with neural networks

During the third semester, you can opt to begin a full-year master’s thesis, or you can choose additional elective courses, including the recommended Humanoid Robotics and Game Theory and Rationality.
The final semester is dedicated to your individual master’s thesis, which can be completed in collaboration with a company or within academia.

Who should apply?

Are you interested in programming, artificial intelligence, and complexity in society and the natural world?
Do you want the skills to contribute to the future of autonomous systems, robotics, and other quickly developing technologies?
Do you want to gain practical experience in machine learning, game theory, and other methods commonly applied to complex systems?
Then apply for the master’s programme in Complex Adaptive Systems.

Prerequisites and selection


A Bachelor's degree or the equivalence to 180 Swedish credit points (p) or 180 ECTS credits at an accredited university. The programme is open to international and domestic students with a degree in the Natural, Engineering, or Mathematical Sciences. At least 30 credits of mathematics (including linear algebra and analysis) and programming. Applicants must prove their knowledge of English: English 6/English B from Swedish Upper Secondary School or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.


Selection is based upon the number of credits from previous university studies, maximum 165 credits.

After graduation

Graduates of the programme receive the degree Master of Science with a major in Complex Adaptive Systems.
The training you receive in modelling and analysing complex systems opens up a wide range of possibilities on the employment market, in software development and consulting, research and development, management, and in the financial sector. You will be prepared to work as an engineer in a wide range of areas depending on your chosen courses. For example, machine learning and artificial intelligence are quickly progressing in many industries and where you would be particularly well-suited to work.
You may also choose to apply for doctoral studies in areas such as physics, statistical physics, systems biology, or computer science.