Modern scientists increasingly rely on computational modeling and data analysis to explore and understand the natural world.

Given the ubiquitous use in science and its critical importance to the future of science and engineering, computational modeling plays a central role in progress and scientific developments in the 21st Century.

This Computational Science programme aims at educating the next generation of cross-disciplinary science students with the knowledge, skills, and values needed to pose and solve current and new scientific, technological and societal challenges.

A programme with a large range of topics:

It is a unique educational programme that treats in a comprehensive way computation as the triple junction of algorithm development and analysis, high performance computing, and applications to scientific and engineering modeling and data science. 

All disciplines in the Sciences are represented in this programme and you can thereby explore and design thesis projects that cover a large range of topics and own interests, from Mathematics and Computational Science to the Physical Sciences and Life Sciences.    

New methods that make challenging problems tractable:

Scientific computing focuses on the development of predictive computer models of the world around us. As studies of physical phenomena evolved to address increasingly complex systems, traditional experimentation is often infeasible.

Computational modeling has become a primary tool for understanding these systems; equal in stature, for the right questions, to analysis and experiment. The discipline of scientific computing is the development of new methods that make challenging problems tractable on modern computing platforms, providing scientists and engineers with new windows into the world around us.

Develop tools to find trends within datasets:

Data science focuses on the development of tools designed to find trends within datasets that help scientists who are challenged with massive amounts of data to assess key relations within those datasets. These key relations provide hooks that allow scientists to identify models which, in turn, facilitate making accurate predictions in complex systems.

For example, a key data science goal on the biological side would be better care for patients (e.g., personalized medicine). Given a patient’s genetic makeup, the proper data-driven model would identify the most effective treatment for that patient.

Career opportunities:

A significant aspect of this programme is the ability to offer new educational opportunities that are aligned with the needs of a 21st century workforce.

Many companies are seeking individuals who have knowledge of both a specific discipline and computational modeling. And candidates who are capable of modeling and understanding complicated systems in natural science, are in short supply in society.

The computational methods and approaches to scientific problems that you will learn when working on your thesis project are very similar to the methods you will use in later stages of your career. To handle large numerical projects demands structured thinking and good analytical skills and a thorough understanding of the problems to be solved. This knowledge makes you unique in the labour market.