Mathematics for Data and Resource Sciences

Program overview

This English-taught Master’s builds on a strong mathematical background—preferably a Bachelor’s in mathematics—and trains students to apply advanced mathematical methods to today’s industrial and environmental challenges. The programme emphasizes handling and interpreting large data sets, scientific machine learning, and the mathematical aspects of resource management and scarcity (for example, issues around rare earth metals and broader planetary constraints). Coursework is designed to link rigorous theory with practical, application-oriented problems.

Students encounter real-world mathematical questions early through a dedicated lecture series that presents up-to-date, problem-centered case clusters. These cases allow learners to immediately apply new techniques to issues drawn from current research and industry practice. The programme also fosters regional industry links: completing an industrial internship of at least four months can be credited toward the Master’s thesis, shortening the maximum thesis period from nine to six months and reducing its required scope.

Two specialised certification tracks are available for deeper professional preparation. One focuses on Mathematical Data Science, emphasising processing and analysis of large-scale data; the other is Geomathematics, which targets mathematical tools for climate-related and geoscience problems. These specialisations help align graduates’ skill sets with concrete career requirements in research, industry, and environmental sectors.

Key facts and requirements

• Preferred prior qualification: Bachelor’s degree in mathematics (ideal foundation)
• Language of instruction: English
• Industrial internship: minimum 4 months; if completed and credited, thesis maximum duration reduced from 9 months to 6 months and thesis scope is adjusted accordingly
• Optional specialised certification available in Mathematical Data Science or Geomathematics, depending on chosen coursework and in-depth study

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Graduates are prepared for careers that require deep mathematical expertise applied to data-intensive and resource-focused challenges. Typical sectors include data science teams in industry, research and development groups, environmental and geoscience organisations, and companies involved in resource extraction, recycling and materials processing.

With training in scientific machine learning and mathematical modelling, alumni can pursue roles such as data scientist, quantitative modeller, research analyst in geosciences or resource management, or continue into doctoral research where advanced mathematical tools are applied to climate, resource and data problems.