Master of Science in Artificial Intelligence Engineering

Artificial intelligence now touches nearly every part of daily life and promises major advances across many sectors. This research-oriented Master of Science builds on the mathematical and computer-science foundations needed to understand and create core AI technologies. You will study the scientific theories, algorithms and methods used to design AI systems and learn how to embed these technologies into real-world environments—examples include media and information systems as well as industrial processes.

The programme also examines AI from interdisciplinary angles: legal, ethical, social and economic questions that arise as AI becomes more widespread are an explicit part of the curriculum. You will gain exposure to a broad, up-to-date selection of subjects with an international perspective, studying at the interface of computer science and mathematics and exploring application areas such as media, Industry 4.0, mobility and transport.

Small class sizes and a strong staff–student ratio mean teaching takes place in compact learning groups, and the university’s chairs and institutes maintain close collaborations with industry and business partners. The degree is taught entirely in English, prepares you for demanding professional roles across private and public sectors (and academia), and provides a direct route to doctoral studies should you choose to pursue a PhD.

Practical information / entry pointers

• Language of instruction: English.
• Research-oriented Master of Science that supports progression to doctoral studies.
• Programme places strong emphasis on small learning groups and industry links.
• Typical application details, exact admission requirements and the full syllabus are available on the programme website—please consult it for official entry criteria and deadlines.

This master's curriculum is organized into a compulsory core and a set of compulsory-elective modules that let you tailor your studies. The core includes an Introduction to AI Engineering lecture with accompanying exercises, an AI Engineering seminar, and the Master's thesis — combining classroom learning, hands‑on practice and an extended research or development project. Together these components ensure you gain both conceptual foundations and the ability to carry out substantial independent work.

The compulsory‑elective portion is split into six focused module groups that cover the technical, practical and contextual aspects of building AI systems. Key areas are:

• Algorithm Engineering and Mathematical Modelling — design, implement, evaluate and optimise deterministic and stochastic algorithms; model discrete and continuous problems and analyse their complexity; strengthen foundations in mathematical logic, stochastics, functional analysis and discrete mathematics.
• Artificial Intelligence Methods — study symbolic and sub‑symbolic approaches (for example reinforcement learning, knowledge representation and deduction systems), underlying learning theories, and the algorithmic/mathematical principles that make AI systems work.
• Artificial Intelligence System Engineering — learn structured development processes, testing and evaluation techniques (e.g. adversarial testing, simulation), data and knowledge modelling, and approaches to operationalise AI systems while assessing security, traceability, reliability, explainability and transparency.
• Artificial Intelligence Applications — explore application domains such as speech, text and media analysis, business information systems and energy informatics, and learn how domain characteristics influence method choice and system design.
• Cross‑Cutting Concerns — address legal, ethical, social and economic implications of AI deployment; complement technical study with language courses, academic writing workshops, soft‑skills seminars and practical training to prepare for professional environments.
• Research Seminars — train to independently survey current research, synthesise findings and present them orally, gaining in‑depth exposure that prepares you for research roles or doctoral studies.

Learning outcomes: graduates will be able to mathematically model and analyse problems, select and implement appropriate AI methods, design and evaluate robust AI systems for real domains, and reflect on the broader societal, legal and ethical dimensions of AI. The programme balances engineering practice and research preparation, equipping international students for technical industry roles, interdisciplinary teams, or continued academic research.

Program requirements (concise)

• Complete the compulsory core: Introduction to AI Engineering lecture + exercise course and AI Engineering seminar.
• Submit and defend a Master's thesis within the AI Engineering area.
• Choose and complete courses from the six compulsory‑elective module groups listed above.
• Participate in research seminar(s) to develop literature review and presentation skills.
• Fulfil cross‑cutting module requirements (legal/ethical/social topics, language and writing workshops, soft‑skills and practical courses).

Graduates are prepared for technical and leadership roles across industry, public sector and academia. Typical positions include systems developer, software/IT systems engineer, data analyst, and AI solution developer for domains such as digital media, finance and services, transport and mobility, Industry 4.0, medical and pharmaceutical sectors, insurance and banking.

The research orientation and in-depth methodological training also provide a clear path to doctoral studies and academic careers in artificial intelligence and related fields.