This course is a hands-on introduction to programming with Matlab and Python, oriented at the needs of civil engineers. The course comprises self-paced tutorials, a project consisting of implementing an engineering application including graphical user interface, and individual meetings with teaching assistants to demonstrate understanding and progress.

Numerical simulation is an essential tool in modern engineering. The course "Scientific Computing" provides Civil Engineering students with both theoretical knowledge of the most important numerical methods and practical experience in implementing them using MATLAB. A mix of lectures, exercises, and project work ensures students can apply these methods to solve real-world engineering problems.

Uncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation and surrogates (Monte Carlo, polynomial chaos, Gaussian processes), and sensitivity analysis.