The master’s programme is a two-year programme. The programme is organized in semesters. Each semester contains 20 weeks, and is subdivided in two quartiles. The unit of credit is the European Credit (EC). One EC stands for 28 hours of study-load. An academic year is 60 EC. The master’s programme is 120 EC.
The educational profile of the programme is characterised on the one hand by the specializations within the programme and on the other hand by the attention paid to mathematical modelling. The four specialisations are based on the corresponding fields of research of the Department of Applied Mathematis:
- Mathematical Systems Theory, Applied Analysis and computational Science (SACS)
- Operations Research (OR)
- Mathematics of Data Science (MDS)
In their first year students take courses corresponding with their specialization and chosen specialization. Year two is used for an internship and a graduation project. During this final phase of the master’s programme, the students act as ‘junior members’ of the chair they have selected. It is during this phase that the students are given the greatest opportunity to demonstrate that they have acquired the qualities outlined in final qualifications by the time they complete their studies.
Applied Analysis deals with the combination of modeling, analysis and simulation of problems from the natural, life and technical sciences with applications neuroscience and medical imaging.
Systems and Control theory has roots in electrical and mechanical engineering. It has applications in, e.g. econometrics, process technology and informatics. The mathematical tools include linear algebra, ordinary and partial differential equations, probability theory.
Computational Science focuses on the mathematical aspects of advanced scientific computing. The two main areas are numerical algorithms for the solution of partial differential equations and mathematical modeling of multi-scale.
Multiscale Modeling and Simulation focuses on the mathematical development and application of computational models for complex physics at micro- and macro scales. The main application areas are in multi-phase flows and phase transitions, biomedical flows and tissue engineering, and self-organizing nano systems.
Both deterministic and stochastic operations research are strongly represented, dealing with Combinatorial Optimization, Mathematical Programming, Supply Chain Management, Queuing Theory, Telecommunications Networks and Industrial Statistics.
Students choose a chair within a specialization: Discrete Mathematics and Mathematical Programming (DMMP) or Stochastic Operations Research (SOR). By including subjects from other chairs of the selected specialization, cohesion is created within the specializations.
The Mathematics of Data Science (MDS) specialization deals with mathematical models and algorithms that can be used to analyse data, to learn and to make decisions. The amount and variety of data that is currently produced is so large that traditional means of processing and storing this data are no longer viable. New methods are, therefore, being developed to manipulate big data sets, to do numerical analysis, and to uncover relations in data. In this master specialization students will learn both about state-of-the-art developments in handling big data and about the mathematics underlying the new methods, including topics like machine learning, spatial statistics and complex networks. A graduate from this specialization will be able to implement and analyse learning, statistical and optimization algorithms and to develop new algorithms that are tailored to specific scenarios. The practical applications of these tools are ubiquitous and are found in, for instance, pervasive health, well-being, intelligent transportation, and business intelligence.
Students can take the Data Science programme within each of the chairs of the Mathematics department or with the Data Science research group.
A solid foundation in statistics, machine learning and operations research is offered and case studies are executed to learn how to leverage the potential of AI for high-stakes real-world applications in healthcare.