Industrial Mathematics and Data Analysis (MSc)

Industrial mathematics, as a young discipline of applied mathematics, focuses on using modern mathematical methods to solve problems from science, engineering, and industry. Recently, mathematical foundations and algorithms for analysing big data have become a key part of this. In four semesters, our newly established international Master's programme provides a broad variety of lectures, seminars, and projects. Students can choose from topics such as machine learning, inverse problems, optimal control, applied statistics, and finite element methods. Central to our Master's programme is the so-called "modelling project": teams of students are assigned real-world problems in cooperation with our partners from industry and institutes, and they use mathematical modelling and analysis combined with numerical simulations to solve these problems. Finally, with the Master's thesis, each student will join an area of current mathematical research. Our Master's programme can be succeeded with a PhD qualification. This Master's programme consists of lectures and seminars on topics regarding industrial mathematics and data analysis. Typically, lectures are accompanied by problem sessions with weekly exercises. For seminars, every participating student works out one topic and presents it to the audience. The specific topics for lectures and seminars vary from semester to semester, the students make their own choices and create individual course plans, according to their personal interests. The common starting point in the first semester are two lectures (mandatory) on "mathematical methods for data analysis and image processing" and on "numerical methods for PDE". Based on these lectures, each student chooses either data analysis or industrial mathematics as a specialisation. In the following semesters, elective courses, i.e., lectures and seminars, are taken in order to specialise and for broadening regarding the other branch, respectively. Possible topics include the following: machine learning, inverse problems, applied statistics, parameter identification (data analysis branch) and optimal control, discrete optimisation, adaptive FEM (industrial mathematics branch). In the heart of this Master's programme is the "modelling project". Teams of students are assigned real-world problems – not from the literature but actual problems from collaborations with engineering institutes or companies. They utilise mathematical modelling, analysis and optimisation techniques to tackle their problems in order to find answers for the customer, i.e., the institute or company that has assigned the problem. In particular, students design, analyse and perform algorithms for numerical simulations, and they visualise their results appropriately. The mathematical part of the programme is accompanied by courses from an area of technical applications. Each student chooses either electrical engineering, mechanical engineering, geosciences, applied physics, or computer science as minor subject. They attend Master's courses on this, offered by the corresponding department. For this, basic knowledge on the chosen subject from Bachelor's studies is indispensable. Last but not least, the complete final semester is designated for the Master's thesis. This is an individual project on a recent research topic, worked out by the student and guided by an expert in this field, i.e., a professor or postdoc. This is summarised in the following study plans. The workload each course entails is implied by the given numbers of ECTS credit points (CP). PDF Download