|Degree:||Master of Science (MSc)|
|University website:||Complex Adaptive Systems|
|Annual tuition (EEA)||0 SEK|
|Annual tuition (non-EEA)||140,000 SEK|
The human brain, economic markets, our immune systems — even the formation of clouds. All examples of complex adaptive systems, formed from multiple interacting components, often non-linear and dynamic, leading to a collective structure and organisation across multiple levels.
The behaviour of complex adaptive systems in nature has served as inspiration for all sorts of advances and methodologies in information processing, from artificial neural networks inspired by neurobiology to genetic algorithms and programming based on natural evolutionary processes — even the design of artificial life. Further examples include the fluctuations of stocks and shares, the dynamics of dust particles in engine exhausts, and earthquake prediction. The challenges of adaptive learning — teaching robots to respond to unexpected changes in their environment, for example, is also an extremely important emerging field.
With a truly interdisciplinary approach, encompassing several theoretical frameworks, this Master’s programme will provide you with a broad and thorough introduction to the theory of complex adaptive systems and their application to the world around us. You will gain the knowledge and tools necessary to model and simulate complex systems, learning how to use and build algorithms for analysis, optimisation and machine learning.
The programme is based on a physics perspective, with a focus on general principles, but we also offer courses in information theory, computer science and optimisation algorithms, ecology and genetics, as well as adaptive systems and robotics. Besides traditional lectures on simulation and theory of complex systems, the programme is largely based on numerical calculation and simulation projects. Depending on your course selection, you will also be able to do practical work in our robotics lab.
The subjects of physics, simulation, modelling, robotics and autonomous systems form the fundamental areas of the master's programme. Elective courses handle a very wide range of topics, including programming, network theory, turbulence, genetics, game theory, biophysics, chaotic dynamics, fractals and dynamical stochastic processes.