Mathematics

Overview

Course Details

The Mathematics MSc course at the University of Potsdam builds on the bachelor’s degree and introduces students to current research questions through individual specialization. The department’s prominent research priorities might be grouped in two distinctive sets of lectures which, however, do have an interesting overlap:

• Structures in mathematics related to a physical background
• Mathematical modeling and data analysis

These profiles complement each other and ensure consistent, reliable and research-oriented teaching in the department. You can choose from the following specializations within these profiles:

• Algebra and number theory,
• Analysis,
• Applied mathematics,
• Discrete mathematics,
• Geometry,
• Geometric analysis,
• Mathematical modeling and systems biology,
• Mathematical physics,
• Numerical mathematics,
• Partial differential equations,
• Statistics,
• Probability theory.

The close connections to non-university research institutions on campus further enrich this offer. On the other hand, a high degree of flexibility is possible, allowing courses from both profiles to be combined or replaced by other courses. In an additional subject such as physics, computer science, life sciences (bioinformatics or animal physiology), cognitive sciences, chemistry, geosciences, economics or business administration, you can acquire additional knowledge with a view to your future professional orientation. Please note that not all additional subjects have a continuous range of courses in English. Mentors will always be available to assist students with any questions they may have regarding the composition of the courses.

Career Opportunities

• The career prospects for mathematicians are excellent and diversified: They work in industry, banks and insurance companies, in the field of ecology, in administrations, research institutes and universities. The fields of application are very diverse: Data processing, development and application of algebraic, analytical, geometrical, numerical and stochastic methods, solution of optimization problems as well as modeling and the simulation of complex issues. Future employers value not just mathematical knowledge, but also the analytical skills that students have acquired during their studies, along with the stamina to solve complex problems.