Programme: Computational Science Master
The course introduces the foundations of the most important stochastic methods in computer simulations. These methods are applicable in a wide range of context that spans from modelling the behavior of network systems to financial systems, to in-silico clinical trials. The main objectives are:
- Understand the foundations of probability theory and how it is applicable to various stochastic processes.
- To be able to construct and evaluate stochastic models to simulate various real-world systems from finance to biomedicine.
- To be able to analyze and test the validity of such models.
- To be able to interpret correctly the predictions of these stochastic models.
Project Computational Science
Programme: Informatics Bachelor
The purpose of this intensive, hands-on course is to successfully complete a computational science project in a small team. The final deliverable is a report, accompanied by a functional software which simulates a real-world phenomenon and performs statistical analysis. It is specifically required for any project to have both a modeling & simulation component as well as a statistical data analysis component. In the previous block in the Minor Computational Science you completed smaller assignments in only one of these two components. In this course you will perform a larger project which combines the two.
Posters from previous years
Thesis project topics
These thesis topics are available every year, and are embedded in ongoing research project. If you are interested, please contact me in email for more details on the specific goals. You can also take a look at the Projects page for some visual representation of similar projects.
- Extreme-resolution fluid dynamics simulation on supercomputers (using lattice Boltzmann).
- Cellular blood flow simulation in various diseases (diabetes, malaria, thrombosis), based on HemoCell.
- Simulation of medical devices and implants, including the implantation procedure.
- Energy measurement of large-scale supercomputer simulations.
- Quantum gravity simulations (large-scale Monte-Carlo simulations on complex graphs).
All of these project require proficiency in Python, and some C++ knowledge is also useful for most.