Complex adaptive systems master's programme at Chalmers
To understand the dynamics of increasingly complex phenomena where standard simulation methods are inadequate, stochastic algorithms, game theory, adaptive programming, self-similarity, chaos theory and statistical methods are used to describe and increase our understanding of complex systems in nature and society, in the end trying to predict the unpredictable. Examples are gene-regulation networks, the motion of dust particles in turbulent air or the dynamics of financial markets.
One example is fluctuations in share and option prices determining the stability of our economy. Other examples are the dynamics of dust particles in the exhaust of diesel engines, the dynamics of biological or artificial populations, earthquake prediction, and last but not least adaptive learning: the problem of teaching a robot how to respond to unexpected changes in its environment.
Truly interdisciplinary and encompassing several theoretical frameworks, this programme provides you with a broad and thorough introduction to the theory of complex systems and their applications to the world around us. You will gain the knowledge and the tools needed to model and simulate complex systems and learn how to use and build algorithms for analysis, optimization and machine learning.
The master's programme is based on a physics perspective with a focus on general principles, but it also provides 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 the theory of complex systems, the programme is largely based on numerical calculations and simulation projects. Depending on your course selection, you will also be able to do practical work in our robotics lab.
Complex adaptive systems master's programme at Chalmers
To understand the dynamics of increasingly complex phenomena where standard simulation methods are inadequate, stochastic algorithms, game theory, adaptive programming, self-similarity, chaos theory and statistical methods are used to describe and increase our understanding of complex systems in nature and society, in the end trying to predict the unpredictable. Examples are gene-regulation networks, the motion of dust particles in turbulent air or the dynamics of financial markets.
One example is fluctuations in share and option prices determining the stability of our economy. Other examples are the dynamics of dust particles in the exhaust of diesel engines, the dynamics of biological or artificial populations, earthquake prediction, and last but not least adaptive learning: the problem of teaching a robot how to respond to unexpected changes in its environment.
Truly interdisciplinary and encompassing several theoretical frameworks, this programme provides you with a broad and thorough introduction to the theory of complex systems and their applications to the world around us. You will gain the knowledge and the tools needed to model and simulate complex systems and learn how to use and build algorithms for analysis, optimization and machine learning.
The master's programme is based on a physics perspective with a focus on general principles, but it also provides 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 the theory of complex systems, the programme is largely based on numerical calculations and simulation projects. Depending on your course selection, you will also be able to do practical work in our robotics lab.
