Complex Adaptive Systems, Master's Programme
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, computer science and optimization algorithms, ecology, and genetics in addition to adaptive systems and robotics.
The programme offers four informal tracks, each with a selection of recommended courses:
- physics/statistical physics
- robotics and adaptive systems
- machine learning and data science
- computational biology/systems biology
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 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. You will also have the opportunity to participate in a student project activity with the Fraunhofer-Chalmers Research Centre for Industrial Mathematics.
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 Complex Systems Seminar course runs throughout the first year. The first semester has four core courses:
- Neural Networks
- Stochastic Optimization Algorithms
- Simulation of Complex Systems
- Dynamical Systems
The second semester has one core course, Computational Biology 1, and the opportunity to choose elective courses, though we recommend the following:
- Information Theory of Complex Systems
- Autonomous Agents
- Computational Biology 2
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.