While the national Mastermath program allows you to follow courses all across the country, the master thesis project should make the difference in your choice of university. Have a look at a list of exciting thesis topics that you could explore at the Vrije Universiteit Amsterdam.

Project: Brain Network Dynamics
Supervisor: Daniele Avitabile and Christian Bick
Short description: The brain, the 'command center' of an organism, can be seen as a network of billions of dynamical units whose activity give rise to brain function. We leverage mathematical tools from dynamical systems theory, to understand how network connections and the dynamics of individual units lead to emergent function and how it may be disrupted in neural disease. This project may involve potential collaborations with neuroscientists.

Project: Studying knots formed viral DNA
Supervisor: Senja Barthel
Short Description: The formation of knot types that appear in viral DNA are studied, allowing to test models of DNA packing. The knots can also be studied in their own right.

Project: Riemannian geometry of brain networks
Supervisor: Rikkert Hindriks 
Short description: Brain networks can be reconstructed from non-invasive measurements such as functional MRI and are commonly characterized by symmetric positive definite (PSD) matrices (i.e., covariance matrices). The Riemannian geometry of the space of PSD matrices has recently been exploited for statistical inference and related tasks (e.g., clustering and regression). In this project we study the geometry of the quotient manifold of normalized PSD matrices (i.e., correlation matrices) and apply the results to simulated or real functional MRI data.

Project: Thriving or surviving: how bacteria manage to stay alive in an ever-changing world
Supervisor: Bob Planqué
Short Description: Bacteria thrive in diverse and hostile environments by balancing growth during favorable conditions and survival in adverse ones, despite having limited information to guide these decisions. This project will use dynamical systems theory, linear programming, convex analysis and control theory to model how cells manage their internal chemical networks and optimize their behavior with incomplete information