Industrial Mathematics and Data Analysis (MSc)

Overview

Industrial mathematics applies contemporary mathematical theory and computational methods to real problems in science, engineering and industry. In recent years the field has expanded to include mathematical foundations and algorithms for analysing large-scale (big) data. This newly established, international Master's programme is taught in English over four semesters and combines lectures, seminars and project work across a wide range of applied topics — for example machine learning, inverse problems, optimal control, applied statistics and finite element methods.

Structure and outcomes

A central feature of the programme is the modelling project: student teams tackle real-world problems provided in collaboration with industrial partners and research institutes, using mathematical modelling, analytical techniques and numerical simulation to devise solutions. Each student completes a Master’s thesis within an active area of mathematical research, and the degree prepares graduates for both industry roles and further academic study; successful completion can lead to PhD admission.

Key programme components / requirements

• Duration: 4 semesters
• Language of instruction: English
• Course formats: lectures, seminars and project work
• Central modelling project with industry and institute partners
• Master’s thesis based on current mathematical research
• Degree outcome: Master of Science (eligible to continue to PhD study)

This programme combines rigorous mathematical theory with hands‑on problem solving in industrial contexts and data analysis. Teaching is delivered through lectures and seminars, typically paired with weekly problem sessions to reinforce learning. Seminars require each student to prepare and deliver a presentation on a chosen topic, helping to build communication skills and deep understanding. In the first semester everyone takes two compulsory courses — "mathematical methods for data analysis and image processing" and "numerical methods for PDE" — which establish the core analytical and numerical techniques used across the programme.

After the first semester students specialise by choosing either the data analysis track or the industrial mathematics track. Elective lectures and seminars let you deepen your specialisation while also taking courses from the complementary branch. Typical data analysis topics include machine learning, inverse problems, applied statistics and parameter identification; industrial mathematics options commonly cover optimal control, discrete optimisation and adaptive finite element methods (FEM). Throughout the programme you design, analyse and implement algorithms, and learn to present and visualise numerical results effectively.

A distinctive, practical component is the team-based modelling project: student teams work on real problems provided by engineering institutes or companies (not textbook exercises). Teams apply mathematical modelling, analysis and optimisation techniques to produce solutions and deliverables for the external partner. The programme also includes a technical minor chosen from electrical engineering, mechanical engineering, geosciences, applied physics or computer science — you take Master's‑level courses in that discipline, so prior undergraduate knowledge in your chosen minor is required. The final semester is devoted entirely to an individual Master's thesis on a current research topic, supervised by a professor or postdoc.

Key requirements and outcomes

• Two mandatory first‑semester lectures: mathematical methods for data analysis and image processing; numerical methods for PDE.
• Weekly problem sessions accompanying lectures; seminars require an individual student presentation.
• Choice of specialisation: data analysis (e.g., machine learning, inverse problems, applied statistics, parameter identification) or industrial mathematics (e.g., optimal control, discrete optimisation, adaptive FEM).
• Team modelling project using real industrial or institute problems — emphasis on mathematical modelling, algorithm design, numerical simulation and result visualisation.
• Minor subject from technical applications (electrical engineering, mechanical engineering, geosciences, applied physics, or computer science); Bachelor‑level foundation in the chosen minor is necessary.
• Final semester reserved for an individual Master's thesis supervised by a faculty expert.
• Workload reflected by ECTS credit points for each course component.

Learning outcomes include the ability to formulate and solve applied mathematical models, implement and validate numerical algorithms, specialise in either data analysis or industrial mathematics, communicate technical results, and work effectively in interdisciplinary and industry‑linked teams.

Graduates are prepared for technically oriented roles where mathematical modelling, numerical simulation and data analysis are central. Typical positions include data scientist, quantitative analyst, computational scientist, simulation/modeling engineer, and specialist roles in R&D departments of engineering and technology companies. The programme’s close collaboration with industry through modelling projects supports direct transitions into applied industry roles.

The curriculum also provides a solid foundation for academic research; graduates who pursue this route can continue with doctoral studies (PhD) in applied mathematics, data science or related engineering and scientific disciplines. The combination of applied mathematics and a technical minor increases employability across sectors such as manufacturing, energy, automotive, geosciences, finance and software companies focused on numerical algorithms and machine learning solutions.