University of Trento

What you'll study

The programme combines core foundations in machine learning, probabilistic models and knowledge representation with applied topics such as computer vision, natural language processing, robotics and autonomous systems. Teaching typically mixes lectures, lab work and project-based courses, and students complete an extended individual thesis or capstone project under academic supervision.

• Core topics: statistical learning, deep learning, Bayesian methods, optimisation and algorithmic foundations of AI.
• Systems and applications: perception (computer vision), NLP, multi-agent systems, planning, control for robotics and embedded AI systems.
• Tools and engineering: software engineering for AI, scalable data processing, experimentation methodology and deployment of AI services.
• Research and ethics: methods for experimentation, reproducibility, AI safety, fairness and legal/ethical implications of intelligent systems.
• Capstone and thesis: a substantial research or industry-oriented project, typically involving experimental evaluation, implementation and a written thesis; opportunities for internships or joint projects with local research centres.

Entry requirements

Applicants are expected to hold a recognised undergraduate degree in computer science, information engineering, mathematics, physics or a closely related discipline, with solid background in programming, linear algebra, probability and statistics. Suitable applicants often have completed coursework in data structures, algorithms and introductory machine learning.

Other typical requirements include:

• Academic transcripts demonstrating relevant quantitative coursework.
• Proof of programming experience (coursework, projects or work experience).
• Evidence of English-language proficiency when the applicant’s prior education was not in English (accepted tests or equivalent certificates).
• Motivation statement and references; selected applicants may be asked for an interview or to provide a portfolio of projects.

Applicants with complementary backgrounds may be admitted subject to passing preparatory bridging modules.

Career prospects

Graduates move into a range of technical and research roles across industry and academia. Typical career paths include:

• Machine learning engineer or data scientist — designing and deploying predictive models and data-driven services in sectors such as software, finance, health and e‑commerce.
• Research scientist or PhD candidate — pursuing doctoral studies or research positions in universities and research institutes.
• Robotics and autonomous systems engineer — working on perception, planning and control for robots and autonomous platforms.
• AI consultant or product specialist — integrating AI solutions into business processes and advising on strategy, ethics and governance.

The programme’s connections with local research centres and industry partners also support internship and placement opportunities that facilitate transition into the labour market.

Why study at University of Trento

The University of Trento is known for strong computer science and AI research, delivered in a compact campus environment that encourages close interaction between students and faculty. Students on this programme benefit from:

• Close collaboration with active regional research centres and labs, offering joint projects and internship options.
• An interdisciplinary approach that links theoretical foundations to systems engineering and practical applications.
• Small cohort sizes and accessible faculty, which support hands-on laboratory work and individual supervision for thesis projects.
• Opportunities to engage with external partners in industry and research, enhancing employability and applied research experience.

Overall, the programme is well suited to candidates seeking rigorous technical training in AI together with practical experience and routes into research or industry roles.