Programme overview

This English-taught Master's is built to give you a thorough, research-grounded education in artificial intelligence with wide applicability across disciplines. The curriculum covers a broad range of AI topics and specialisations, supported by numerous research groups within the Department of Computer Science. Compared with data-science-focused degrees, the programme places stronger emphasis on the constructive design and implementation of algorithms, data structures, neural networks and their concrete applications.

The degree lasts a minimum of four semesters. Teaching includes lecture-based courses as well as practical, hands-on project work and lab sessions that let you apply concepts to real problems. The final semester is primarily reserved for the Master’s thesis, which is intended to introduce students to independent research work at the university and to consolidate skills gained during the programme.

Entry requirements (concise)

• Bachelor's degree in informatics/computer science (central qualification for admission)
• Degrees in closely related fields may be considered; such applicants could be asked to sit an oral entrance examination to verify necessary prior knowledge
• Instruction is in English; applicants should meet the university's English-language proof requirements (see application information)
• Intake available in both winter and summer semesters
• Full application details and required documents are provided on the university’s application webpage; consult that page for deadlines and precise submission instructions

Curriculum summary

This MSc is a 120 ECTS programme normally completed in four semesters (30 ECTS per semester). The curriculum combines taught courses, project work, a seminar, a minor, and a research thesis so that students both deepen their AI knowledge and gain hands-on research and development experience. For precise rules on credit distribution and progression, consult the official study regulations (e.g., the FPO) and the semester module catalogue, which lists available courses each term.

The taught portion is dominated by 50 ECTS of electives designed to introduce students to current AI research and let them tailor their profile. Electives are organised around three central pillars — Symbolic AI, Subsymbolic AI, and AI Systems & Applications — and encourage either theoretical depth or applied skill development depending on your interests. A 15 ECTS minor lets you explore complementary, practice-oriented topics that intersect with AI, broadening your perspective for future careers.

Hands-on and research-focused elements include two 10 ECTS projects (each ~300 hours across ~6 months) that test your technical, research and self-organisation skills; a 5 ECTS seminar that develops your ability to investigate a topic and present results (group work is possible); and a 30 ECTS Master’s thesis (~900 hours, six months) that must relate to subjects studied in the programme. The thesis is written in English, typically supervised by an assigned adviser, may involve participation in a research group, and culminates in an oral presentation of about 30 minutes followed by discussion. These components are intended to produce graduates able to conduct independent AI research, work in teams on complex problems, and communicate technical results clearly.

Key requirements and constraints

• Total workload: 120 ECTS (typically 4 semesters, 30 ECTS/semester)
• Electives: 50 ECTS total, organised around three pillars (Symbolic AI, Subsymbolic AI, AI Systems & Applications)
  • Minimum 10 ECTS in each pillar
  • Available courses listed in the semester module catalogue
• Projects: two projects of 10 ECTS each (~300 hours, ~6 months each)
  • Each project must be associated with one of the three pillars
  • The two projects cannot both be associated with the same pillar
• Seminar: 5 ECTS (can be completed in a group; involves research and presentation)
• Minor: 15 ECTS of practice- or topic-linked courses related to AI (not duplicating electives)
• Master’s thesis: 30 ECTS (~900 hours, 6 months to complete)
  • Can be registered after earning 70 ECTS total
  • Topic must relate to programme subjects; adviser normally assigned
  • Written in English and concluded with an approx. 30-minute oral presentation plus discussion

If you want to specialise further or prepare for PhD-level work, the elective and project choices — together with a substantial thesis — are structured to build research competency, independent problem solving, and industry-relevant technical skills.

Graduates will be well prepared for technical R&D and engineering roles in industry and research institutions, including positions as machine learning engineers, AI/algorithm developers, software engineers for AI systems, and applied research scientists. The programme’s strong emphasis on algorithmic implementation, programming, and mathematical foundations makes graduates attractive to technology companies, startups, and sectors deploying AI (robotics, autonomous systems, healthcare, finance, etc.).

The research-focused components (projects and thesis) also provide a solid pathway to doctoral studies (PhD) for students interested in continuing in academic or advanced industrial research careers.