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Quarter 1
Artificial neural networks (FFR 135)
The course explains purpose, theory, and applications of neural networks. Thís includes models of associative memory, algorithms for learning from examples, and models of self-organization.
Stochastic Optimisation (Evolutionary computation)
The course introduces the students to new methods in computer science inspired by evolutionary processes in nature. Examples are genetic algorithms, genetic programming, and artificial life.
Complex systems seminar
Throughout the first year, a seminar series is held in which the students present and discuss recents findings and results reported in the scientific literature. |
Quarter 2
Simulation of complex systems
The course introduces methods for modelling and computer simulation of complex systems, such as game theory, network models, Monte-Carlo simulation, and cellular automata.
Computational biology A (FFR 110)
The course provides an introduction to modelling macroscopic biological systems. Topics discussed are population dynamics and ecosystems, gene regulation, enzymatic reactions, morphogenesis and pattern formation, and time-series analysis.
Complex systems seminar continues
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Quarter 3
Stochastic processes in physics, chemistry, and biology
Systems in nature are usually complex in that they consist of many interacting particles of different components. Examples are colloidal dispersions, polymer solutions and melts, gels, glasses. Since the microscopic structure of such systems are only partially known, their time evolution is usually modelled by a stochastic process.
This course introduces the most important stochastic processes and their properties. It is shown how to describe natural phenomena observed in physical, biological, and chemical systems
Dynamical systems (FFR 130)
The course gives an introduction to the theory and description of nonlinear dynamical systems. How is chaos measured and characterized? How can one control and predict chaotic systems?
Autonomous Agents
The course aims at giving the students an understanding of design principles for autonomous systems, both robots and software agents, and also gives students the opportunity to apply their knowledge in practice by constructing a simple autonomous robot.
Information theory FFR050
The aim of this course is to give an understanding of fundamental concepts used to describe complex systems.
As examples, chaotic low-dimensional systems, self-organizing systems, and cellular automata are discussed.
Complex systems seminar continues
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Quarter 4
Computational biology B (FFR 115)
The second course in this field aims at giving a basic understanding of computational biology and theoretical models in molecular biology. This will include models of the origin of life, molecular evolution, and molecular genetics.
Autonomous Agents continues.
GRE course, aimed at students applying to
graduate programs in physics.
The GRE exam is required or recommended for graduate studies in many universities, particularly the United States. This course serves two goals: the first is to review undergraduate physics and to provide the student a chance of doing as well as possible in the GRE exam. A benefit of the course is that it will provide feedback to both faculty and students about the effectiveness of our undergraduate education. The course will also be a forum for advice and discussions about doing Ph.D. studies in physics.
Complex systems seminar continues.
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