The brain, the immune system and the formation of clouds, are all examples of complex adaptive systems comprising of many interacting components, often non-linear and dynamic, leading to multiple levels of collective structures and organization.
Inspired by complex adaptive systems in nature, several new methods for information processing have emerged: artificial neural networks resemble neurobiology; genetic algorithms and genetic programming are based on evolutionary processes in nature; the construction of artificial life, the design of autonomous robots and software agents are based on the behaviour of living systems.
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 of 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 its applications to the world around us. The 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 theory of complex systems, the programme is largely based on numerical calculation and simulation projects and depending on course selection possibly practical work in the robotics lab.
The subjects of physics, simulation, modeling, robotics and autonomous are fundamental areas in the
Complex Adaptive Systems programme. The courses handle topics such
as programming, turbulence, genetics, game theory, biophysics, chaos and
dynamical stochastic process
Master's programme structure
The master's programme runs for a duration of two years. 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 the programme starts with six compulsory courses that form a common foundation in Complex Adaptive System. Each course is usually 7.5 credits.
- Artificial neural networks
- Simulation of complex systems
- Stochastic optimization algorithms
- Dynamical systems
- Complex systems seminar
- Computational biology 1
Compulsory courses year 2
In the second year you must complete a master's thesis in order to graduate. The thesis may be worth 30 credits or 60 credits depending on your choice.
Compulsory elective courses
Through compulsory elective courses, you can then specialize in physics/statistical physics, robotics and adaptive systems, machine learning and data science or computational biology/systems biology. During year 1 and 2, you need to select at least 2 compulsory elective courses in order to graduate.
Suggested profile courses
- Quantum mechanics
- Non-equilibrium processes
- Turbulence modelling
- Computational physics
- Computational fluid dynamics
- Information theory for complex systems
Robotics and adaptive systems
- Intelligent agents
- Humanoid robotics
- Autonomous robots
Machine learning and data science
- Algorithms for machine learning and inference
- Statistical learning for big data
- Game theory and rationality
- Deep learning
- Introduction to AI
Computational biology/Systems biology
- Computational biology 2
- Computational methods in bioinformatics
- System biology
Other Master's programmes that might interest you
Computer Systems and Networks, MSc
Engineering Mathematics and Computational Science,MSc
Software Engineering and Technology, MSc
Systems, Control and Mechatronics, MSc
Entry requirements (academic year 2021/22)
General entry requirements
An applicant must either have a Bachelor's degree in Science/Engineering/Technology/Architecture or be enrolled in his/her last year of studies leading to such a degree.
Specific entry requirements
Bachelor’s degree with a major in: Engineering Physics, Physics, Electrical Engineering, Mechanical Engineering, Automation and Mechatronics Engineering, Computer Science, Computer Engineering, Mathematics, Chemical Engineering, Chemistry, Bioengineering or the equivalent
Prerequisites: Mathematics (at least 30 cr. including Linear algebra and Mathematical analysis) and Programming
English language requirements
Chalmers Bachelor’s degree
Are you enrolled in a Bachelor’s degree programme at Chalmers now or do you already have a Bachelor’s degree from Chalmers? If so, different application dates and application instructions apply.
Degree: Master of Science (MSc)
Duration: 2 years
Level: Second Cycle
Rate of study: 100%
Instructional time: Daytime
Language of instruction: English
Teaching form: On-campus (Location: Campus Johanneberg)
Tuition fee: 140 000 SEK/academic year
Application Code: CTH-11009
Application Period: Mid-October - Mid-January every year
Specific questions about the programme:
Mats Granath, Director of Master's Programme, firstname.lastname@example.org
Training in "computational engineering" teaches students to model and analyse complex systems and the computer modelling and analytical skills acquired in the programme open up a wide range of possibilities on the employment market, in software development and consulting, in research and development, management, and in the financial sector.
The content of the master's programme is closely connected to the research on genetics and turbulence, information theory and adaptive systems and robotics performed at Chalmers 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.
The programme also has a student project activity with the Fraunhofer-Chalmers Research Centre for Industrial Mathematics
“I have made a Siri-app for football”
Dante Landa Vega, Mexico, Complex Adaptive System
Why did you choose this programme?
– I have a background in mechatronics and was very interested in Artifical neural networks. All the research in the field is now going into machine learning and deep learning is everywhere, in our cellphones, our computers and tablets and soon in our vehicles too. There seems to be a great demand in the industry for people that can develop Artifical intelligence and I wanted to be a part of that.
What have you been working on?
– I have created an intelligent agent like Siri, but it works only for football teams. I named it Fabio after a Mexican tradition that I have with my friends when we play FIFA. You can ask Fabio questions about your favourite football team and it returns information about when they are playing the next match, their results and images. It works with three different leagues: The Premier League in England, Bundesliga in Germany and La Liga in Spain. The application itself is in the shape of a football and when it finds some information it stars to bump and rotate.
What do you like the most about your programme?
– The combination of theory and practice. Everything is built from scratch from a mathematical model, then passed on to code and then a system. And everything works from my first idea. When I finish a project, it keeps growing and improving without me which is very cool.
What do you want to do in the future?
– I would like to work with machine learning or deep learning for a company here in Sweden rather than having my own business or going for a PhD.