Mathematical Economics

Mathematics is a very old and traditional science and at the same time is so important for our future. Whether IT, artificial intelligence, Data Science or finance - no future technology can do without mathematics. As a Mathematical Economics graduate, you can not only work mathematically precisely, but also translate problems from business and technology into the language of mathematics, solve them using mathematical models and at the same time assess the potential of these models. With these skills, you can make a significant contribution to developing answers to challenging (future) questions. Mathematical Economics graduates are in high demand on the labour market, e.g. as data scientists or analysts, actuaries for insurance companies or banks, or IT consultants for AI applications to tackle the challenges of modern society.

As part of the master's course, you will be introduced to current research questions in Mathematical Economics and learn to plan, carry out and complete a research project independently, i.e. in addition to specialist expertise, you will also acquire professional project management skills.
The Master's course in Mathematical Economics at Bielefeld is characterised by its three profiles Mathematics, Economics and Finance, which offer you a thematic focus and at the same time a wide range of choices.

Bielefeld University is traditionally known for interdisciplinary research and teaching, especially in the area between mathematics and economics. Long-term collaborative research projects in this area and, in particular, the Institute of Mathematical Economics (IMW) create an attractive, international research environment with particular appeal for teaching (for example in the form of numerous specialisation options) in the field of Mathematical Economics.

As in any (business) mathematics degree programme, learning and working together is very important. You work very closely together in teams and are also individually supervised by students from higher semesters, doctoral students and postdocs (tutors) in tutorials accompanying the lectures.