Focus areas

Below are short descriptions of the 10 focus areas on the master's programme in Mathematical Modelling and Computation. In depth descriptions—including lists of recommended courses—are available for each focus area.

We recommend that you obtain competences from more than one focus area.

To get some ideas of what students actually do you can download an overview of which courses 17 students actually took. Each column is one student and the number indicates at which semester a course was taken.

1. Applied mathematical analysis

Applied Analysis provide important mathematical methods that are widely used in engineering, natural science, and industrial problems. Prototypical examples include motion analysis, material science investigations, and shape optimization.

2. Industrial and Applied statistics

The field of industrial statistics has been receiving a renewed interest with expanding use of quality and productivity improvement methodologies such as Six Sigma. This focus area will equip the student with the practical statistical analysis tools required in today’s data rich environments.

3. Scientific computing

Applications of mathematics in the analysis, modelling, and solution of complex engineering problems often involve scientific computing—a combination of advanced mathematical techniques, high-performance computing, computer simulations and methods for optimization, data analysis and visualization.

4. Stochastic dynamical modelling

Tools for analysing and modelling dynamical systems based on available time series of data are more and more applied within important areas like finance, pharmaceutics, biology, and energy production (wind, solar, ..).

5. Operations research for decision making

Operations Research (OR) apply mathematical methods to real-world planning problems. Operations Research was initiated during the Second World War, and has been applied extensively since then. Today many important planning problems are solved using mathematical optimization methods—often through the use of advanced software.

6. Secure and reliable computing

This focus area will provide students with skills from applied mathematics and computer science. These skills are essential to constructing the modern, pervasive IT and communication systems that form, and will form, the infrastructure of our society.

7. Image analysis and computer graphics

Image analysis and computer graphics play decisive roles in automating processes and in our daily lives. Both are closely linked to statistics, algorithms, scientific computing, optics, camera/sensor technology, graphics hardware, and application areas—e.g. life science, medical imaging, food control, and geoinformatics.

8. Cognitive science and technology

Cognitive science and technology uses mathematical models and data analysis as well as behavioural and neurophysiological experiments to understand human cognition and brain function. Our aim is to use our understanding in the development of new information and communication technologies.

9. Machine learning and signal processing

The explosion in the availability of data from the internet and modern sensor technology (in neuroscience, bio-medicine, etc.) offers new possibilities for science and technology, but it also poses huge demands on the development of methodology and computational tools. Statistics, mathematical modeling, and computational methods come together in machine learning to form solutions to these large-scale data engineering problems.

10. Financial engineering

Financial engineering is a multidisciplinary field relating to the creation of new financial instruments and strategies. Financial engineers are normally employed in the banking industry—either as quantitative analysts or as part of product and strategy development or risk modelling and management teams.