Master of Science in Computational Sciences

Overview The Master of Science in Computational Sciences is a four-semester, English-language programme that trains students to use rigorous mathematical and computer-based methods and to develop software solutions for contemporary scientific problems. Teaching staff come from a range of disciplines — chemistry, physics, geosciences, mathematics and computer science — giving the course a strongly interdisciplinary character. The curriculum mixes theoretical foundations with application-oriented work so students can both understand methods deeply and apply them to real research questions.

Structure The programme is organised into four core components that together build breadth and depth across computational and domain science:

• Synchronisation: courses that teach fundamental computational-science methods and fill gaps from students’ bachelor degrees to create an interdisciplinary profile.
• Scientific computing: focused tracks that allow students to emphasise numerics, statistics, or computer science.
• Specialisation: concentration in one of three applied research areas — molecular sciences, geosciences, or atmospheric sciences.
• Master’s thesis: an independent research project written up in the chosen area of specialisation.

What you will gain Students acquire combined mathematical, computational and applied-science expertise and develop interdisciplinary problem-solving skills. The programme fosters independent scientific thinking, software development ability for research, and communication skills through preparing lectures and reports. Practical work and supervised tutorials also address gender and diversity issues and train students to collaborate effectively in the international, multicultural research groups that participate in the programme.

Key facts and components (concise)

• Duration: 4 semesters (full-time)
• Language of instruction: English
• Teaching disciplines represented: chemistry, physics, geosciences, mathematics, computer science
• Main components: Synchronisation, Scientific computing (numerics/statistics/CS priority), Specialisation (molecular/geosciences/atmospheric), Master’s thesis
• Outcomes: advanced computational methods, software development for scientific problems, interdisciplinary research competence, oral and written presentation skills, experience in international/multicultural teams

Program structure — at a glance

The Master’s program comprises 120 credit points (CP) and is organised into four 30‑CP components: a Synchronisation area, a Scientific Computing area, a Specialisation area, and a Master’s thesis with an accompanying colloquium. The curriculum is built to bring students from diverse undergraduate backgrounds up to speed in computational methods and to provide in‑depth domain expertise through focused electives and a research project.

Key modules and specialisation pathways

• Synchronisation (30 CP): This block fills subject‑specific gaps for students coming from different disciplines. Available subject streams include Chemistry, Geographical Sciences, Geological Sciences, Computer Science, Mathematics, Meteorology, and Physics. The Computational Sciences module is mandatory within this area to ensure a common computational foundation.
• Scientific Computing (30 CP): Students choose two modules from core computational topics — Computer Science, Numerics, and Statistics — to develop algorithmic, numerical and data‑analysis skills that underpin modern computational science.
• Specialisation (30 CP): Choose one profile to gain domain depth:
  • Molecular Sciences: Training for careers in computational chemistry/physics and applied mathematics with applications in biotechnology, nanotechnology, and pharmaceutics.
  • Geosciences: Focus on analysing and forecasting geological processes, assessing climate and ecosystem impacts, economic risks, and public communication of geoscientific issues.
  • Atmospheric Sciences: Emphasis on modelling and predicting short‑term dynamics (weather) and long‑term statistics (climate); atmospheric and Earth‑system modelling are central. Each specialisation combines compulsory electives, optional electives, and a scientific project. The project can be carried out in cooperation with external institutions and prepares you for the Master’s thesis.

Master’s thesis and learning outcomes

The Master’s thesis (30 CP), typically completed in the fourth semester after at least the Synchronisation area is finished, culminates in a written thesis and a colloquium; students present progress during the thesis period. Graduates will have demonstrated the ability to integrate computational methods with domain knowledge, design and carry out independent scientific projects, critically analyse results, and communicate findings—preparing them for research or industry roles in interdisciplinary computational science. For full details consult the programme website and the official study regulations.

Requirements (concise)

• Total credits: 120 CP (4 × 30 CP components)
• Synchronisation area: 30 CP; choose from Chemistry, Geographical Sciences, Geological Sciences, Computer Science, Mathematics, Meteorology, Physics
• Mandatory: Computational Sciences module within the Synchronisation area
• Scientific Computing area: 30 CP; select two modules from Computer Science, Numerics, Statistics
• Specialisation area: 30 CP; choose one profile (Molecular Sciences, Geosciences, Atmospheric Sciences); includes compulsory and optional electives plus a scientific project (external cooperation possible)
• Master’s thesis + colloquium: 30 CP; usually completed in semester 4 after at least Synchronisation is finished; includes progress presentation

Graduates are prepared for careers in research and development both in academia and industry. Typical roles include computational chemist, computational physicist, applied mathematician or modeller in sectors such as biotechnology, nanotechnology, pharmaceuticals, environmental consulting, climate and weather services, and geoscience-focused organisations.

The programme's strong computational and interdisciplinary competencies also suit positions in data-driven industries, public institutions involved in environmental risk assessment and policy, and doctoral studies. The scientific project and thesis components provide hands-on experience that eases transition to research roles and partnerships with external institutions.