Complex adaptive systems, MSc

120 credits (2 years)

Sign up for information 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. 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 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.​

Topics covered

The subjects of physics, simulation, modeling, robotics and autonomous are fundamental areas in the Complex adaptive systems master's programme. The courses handle topics such as programming, agent based modelling, network theory, turbulence, genetics, game theory, biophysics, morphogenesis, synchronization, chaotic dynamics, fractals and dynamical stochastic process.

Master's programme structure

The master's programme runs for a duration of two years, leading to a Master of Science (MSc) degree. 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
  • 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.
  • Master’s thesis

Compulsory elective courses

Through compulsory elective courses, you can make your own profile and 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 3 compulsory elective courses in order to graduate.

Suggested profile courses

Physic​s/statistical Physics​

  • 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
  • Statistical learning for big data
  • Game theory and rationality
  • Deep machine learning
  • Introduction to AI

Computational biology/Systems biology

  • Computational biology 2
  • Computational methods in bioinformatics
  • System biology

Career

Computer modelling and programming skills, together with expertise in a range of modern algorithms, such as deep machine learning and stochastic optimization, acquired in the programme, open a wide range of possibilities on the job market. Typical employments are often related to data science or advanced engineering topics. For example, in the field of intelligent control systems, such as the development of autonomous driving.  

Previous students from this programme often find their jobs at larger technology-intensive companies such as Volvo, Volvo Cars, Ericsson, Saab, AstraZeneca, Scania, etc., or smaller start-ups. Some of our previous students have also chosen to continue towards a PhD in a wide spectrum of academic fields ranging from computer science to physics and biotechnology.  

Research within Complex adaptive systems

The teachers of the master's programme are active researchers in areas closely related to the programme, such as adaptive systems and robotics, genetics and turbulence, machine learning applications in soft matter physics and quantum physics, information theory and game theory. In addition, there is a wide range of related research activities at Chalmers and the University of Gothenburg, in areas such as deep machine learning, natural language processing, autonomous systems and automation, etc., that provide opportunities for elective courses and master thesis project work.
​​​​​​​​​​​

Sustainable development​

The programme’s focus on modelling complex systems is very relevant to building the knowledge and understanding needed to address several of the UN Sustainable Development goals (SDGs)​The table below provides an overview of the sustainable development goals and the associated targets within the programme.
 UN Sustainable Development goals (SDGs) for Complex adaptive systems at Chalmers



Goal 9: Industry, innovation and infrastructure
Development of the infrastructure of the future will surely involve advanced knowledge of complex systems, autonomous systems, machine learning and other algorithms which are core topics in this master's programme. 

Goal 13, 14 and 15: Climate action, Life below water and Life on land
All these three goals require advanced modelling to understand the threats and the implications of action or lack of action. Courses such as Computational Biology, Dynamical systems and Simulation of complex systems give a good understanding of and respect for the complexity of such problems, and the basic skills needed to further develop the relevant science. 

Student interview

“The robotics track pulled me in
Jamie, USA, Complex adaptive systems

Jamie, student at ChalmersWhy did you choose this programme?

– I was after something related to robotics. In fact, there’s no shortage of programmes specific to robotics at universities around the world. At the end of the day, though, my background is in electrical engineering, specifically embedded software, and I knew what I really wanted to focus on was the decision-making of robotics. What attracted me to this programme at Chalmers specifically was reading about the robotics track, right here on the programme page. If you’re interested in robotics, look at the course plan and read the descriptions for Intelligent agents, Humanoid robotics, and Autonomous robots - you might just be hooked, too! I felt that these classes are the kinds of courses that would justify having slaved away in math and science courses my whole life.

What have you been working on?

– In the first study period of the programme, the required courses are Artificial neural networks and Stochastic Optimization Algorithms. From the title of the latter, you wouldn’t be able to tell that the optimization algorithms we learn about are actually inspired by nature. The coolest algorithm to me was something called “linear genetic programming.” Essentially, the goal is to optimize computer programmes (instruction sets) using concepts from biological evolution, such as codifying the instructions into chromosomes and performing crossover and mutation. Having some background in both biology and computer engineering, I was in awe of how concepts from one field could be applied to a completely different field to produce such beautiful results. I should emphasize, though, that you don’t need a background in either to learn about these algorithms.

What do you like the most about your programme?

– You can tailor your experience to your own interests. That way, every course you take is genuinely interesting to you. Talking to other students in my programme, I was amazed at how excited everyone was about the classes they registered for next semester. Some of my friends are on more of a physics track, some are more interested in big data, and some (like me) are signed up for all the robotics courses. Reflecting our diverse interests are our diverse backgrounds; my peers have studied everything from mechanical engineering, physics, computer science… the list goes on.

What do you want to do in the future?

– I’m hoping that a background in embedded software engineering combined with knowledge and skills from Complex adaptive systems will enable me to approach problems with a broader, multidisciplinary perspective. Where will this lead me? Well, if I’m dreaming big, my career goal would be to work in the space industry helping design robots in the name of space exploration! 

​S​tudent Blogs

Page manager Published: Wed 27 Apr 2022.