Target group 
Master's Programme in Materials Research is responsible for the course.
Modules where the course belongs to:
 MATR300 Advanced Studies in Materials Research.
Optional for:
 Study Track in Computational Materials Physics
 Study Track in Medical Physics and Biophysics
 PAP300 Advanced Studies in Particle Physics and Astrophysical Sciences.
Optional for:
 Study Track in Astrophysical Sciences
 Study Track in Particle Physics and Cosmology
 TCM300 Advanced Studies in Theoretical and Computational Methods
The course is available to students from other degree programmes. 
Timing 
The course can be taken at any time, when it is available.
The course is given annually during the third teaching period (spring term). 
Learning outcomes 
After completion the course you will be able:
 Generate uniform and nonuniform random numbers by using different methods
 Apply pseudo and quasirandom numbers for different tasks
 Perform Monte Carlo integration of multidimensional functions
 Estimate the statistical error of the mean for different methods
 Generate the synthetic data to improve on estimation of the average and the error of the mean
 Improve the convergence of the Monte Carlo integration result using different methods
 Create your own Game of life by using the Cellular automata principle

Completion methods 
The attendance of the lectures is recommended. Returning home completed exercises is mandatory. The exercises are aimed to test the programming skills of students. These will contribute equalliy to the final grade of the exam along with the answers to the exam questions. 
Prerequisites 
The programming skills are mandatory. Basic knowledge of probability theory is recommended. 
Recommended optional studies 
Monte Carlo in Physics 
Contents 
Uniform random numbers
 PseudoRandom Number Generators (RNG):
 linear algorithms: congruential and generalised feedback shift register(GFSR)
 nonlinear algorithms: developments of congruential and twisted GFSR and Mersenne Twister RNG
 Stratified methods
 Quasi RNG
Nonuniform random numbers
 Inversion, hit and miss and combined methods
 Markov chain
Monte Carlo integration, improving convergence of the Monte Carlo integration
Analysis of Monte Carlo integration result: estimation of the error of the mean
Generation of synthetic data to improve the analysis
Cellular automata and selforganized critical phenomena 
Study materials and literature 
 Lecture notes and Supplementary material
 Numerical Recipes in C,
 The art of scientific computing, 2nd edition
 W.H. Press, S.A. Teukolsky, W.T.Vetterling, B.P.Flannery

Activities and teaching methods in support of learning 
Exercises are designed to help students to understand better the material of the course. Regular programming will help to implement the received knowledge during the course in practice. 
Assessment practices and criteria 
The final exam is held in form of answering theoretical questions in form of essays, however, the grade for the exercises performed during the course give 50% of the total weight. 