Vilnius Gediminas Technical University

What you'll study

This master's programme combines core AI theory with hands-on engineering practice. Teaching covers probabilistic and statistical foundations of machine learning, deep learning architectures, reinforcement learning, and optimisation methods. Students also study applied areas such as computer vision, natural language processing, data mining, and robotics, together with supporting subjects in big data processing, distributed systems and software engineering for AI.

• Core modules: Machine Learning; Deep Learning; Probability and Statistical Methods for AI; Reinforcement Learning; Mathematical Optimisation.
• Applied modules: Computer Vision; Natural Language Processing; Data Mining and Knowledge Discovery; Intelligent Robotics and Autonomous Systems.
• Systems and engineering: Big Data Technologies; Cloud and Distributed Computing for AI; Software Engineering for Intelligent Systems.
• Complementary topics: AI Safety and Ethics; Human–Computer Interaction for Intelligent Systems; Project Management.
• Practical experience: Laboratory classes, programming assignments, and team projects using Python and common AI frameworks (e.g. TensorFlow, PyTorch). The programme culminates in an individual research thesis or an industry-oriented master's project supervised by academic staff.

Entry requirements

Applicants are normally expected to hold a recognised bachelor's degree or equivalent in computer science, information technology, electrical engineering, mathematics, or a closely related technical discipline. You should have prior exposure to programming, algorithms and linear algebra; documented coursework or practical experience in these areas strengthens an application.

• Academic transcript and diploma from a recognised institution.
• A CV and a personal statement outlining relevant experience and motivation for advanced study in AI.
• Proof of English language proficiency if your previous degree was not delivered in English (accepted evidence typically includes recognised test scores or institutional certification).
• References or letters of recommendation may be requested, and some applicants could be invited for an interview or technical assessment.

Career prospects

Graduates from this programme are prepared for technical and research roles across industry and academia. Typical career paths include roles such as Machine Learning Engineer, Data Scientist, AI Researcher, Computer Vision Engineer, Natural Language Processing Engineer, and Robotics Engineer. The programme also equips students to work in adjacent functions such as AI product development, technical consultancy, and systems architecture.

Many alumni find employment in technology companies, start-ups, industrial R&D centres, finance and healthcare firms applying AI to real‑world problems, or continue to doctoral studies and research careers. The programme’s industry project option and connections with local and regional technology firms support transitions into practitioner roles.

Why study at Vilnius Gediminas Technical University

Vilnius Gediminas Technical University (VILNIUS TECH) has a strong tradition in engineering education and applied research, with dedicated laboratories and research groups working in AI, robotics and data science. The university emphasises practical, project-based learning and maintains collaborations with industry partners and research institutions in Lithuania and internationally.

Studying in Vilnius offers access to a growing tech ecosystem, opportunities for internship and industry collaboration, and an international student community. The programme’s focus on both theoretical foundations and software engineering for AI prepares graduates to tackle complex, deployed systems as well as to pursue further research in the field.