Complex systems, for instance, in the biosciences, physics, chemistry and medicine, often exhibit complex behavior, whose understanding can provide substantial new insights. The ability to predict such behavior hinges upon the availability of descriptive numerical models, often based on differential equations.

This core track provides students with the necessary theoretical and practical skills to develop such models, to simulate them and draw relevant conclusions. Students learn to exploit adjustable parameters and inputs into the model to fine-tune and optimize the underlying system's behavior. State-of-the-art tools from simulation technology, parallel computing, parameter estimation and large-scale optimization prepare students to act as mathematical modellers, simulation specialists and computational engineers across a wide range of disciplines in research and industry.

The theoretical part of this core track covers modern simulation tools for differential equations as well as advanced optimization with differential equation models. Courses in this area include finite elements, parallel computing, and optimization with differential equations. This theoretical background, combined with the training in their selected application area enables students to approach real-life problems systematically and competently.

Students in this core track are taught by faculty engaging in research in modelling, simulation and optimization, ensuring exposure to the state-of-the-art methods. Moreover, students have the opportunity to work closely with research teams and enjoy mentorship from recognized experts.