Focus areas - Mathematical Modelling and Computation
Mathematical Modelling and Computation spans a wide range of specializations, and to assist you in making a cohesive study plan, we have a number of focus areas that can serve as inspiration:
- Applied mathematical analysis
- Computational modelling and simulation
- Discrete mathematics and secure computing
- Machine learning and data science
- Operations Research and Optimization
Students may combine these areas based on their personal profile and career objectives. Note that the focus areas are for guidance purposes only and will not be explicitly mentioned on the diploma.
Below are short descriptions of the five focus areas on the master's programme. 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.
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. Computational modelling and simulation
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, ..).
3. Discrete mathematics and secure 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.
4. Machine Learning and Data Science
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.
5. Operations Research and Optimization
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.