University of Turin

What you'll study

This programme combines core topics in artificial intelligence with the theory and practice of high performance computing (HPC). Teaching typically covers machine learning and deep learning methods, probabilistic modelling, optimisation, and data engineering alongside parallel programming models, GPU computing, distributed systems and performance analysis. The course structure mixes lectures, practical laboratories and project work, and culminates in a substantial master's thesis or an industry research project.

• Core AI topics: supervised and unsupervised learning, neural networks and deep learning, probabilistic graphical models, reinforcement learning and explainable AI.
• HPC and systems: parallel architectures, MPI and OpenMP programming, GPU programming (CUDA/OpenCL), distributed computing frameworks and performance profiling and tuning.
• Data and software engineering: scalable data pipelines, databases for large datasets, model deployment, containerisation and reproducible research practices.
• Applied modules and labs: hands-on projects applying AI techniques to large-scale datasets, optimisation for performance, benchmarking and case studies from scientific computing, engineering and industry.
• Thesis/project: an independent research project or an industry placement applying AI and HPC tools to a real-world problem under academic supervision.

Entry requirements

Applicants are normally expected to hold a relevant bachelor’s degree in computer science, computer engineering, information engineering, mathematics, physics or a closely related discipline demonstrating good grounding in programming, discrete mathematics, linear algebra and probability. Practical experience with programming (Python, C/C++ or similar) and familiarity with basic data structures and algorithms is required. Where the bachelor’s background is not directly aligned, applicants may be asked to demonstrate additional preparation through bridging courses or documented practical experience.

International applicants must meet the university's language requirements; proficiency in English (and in some cases Italian for local activities) is typically required. Selection may be based on academic transcripts, a statement of purpose, letters of recommendation and, where applicable, interview or technical assessment.

Career prospects

Graduates are prepared for roles that require both AI expertise and experience with large-scale computing. Typical career paths include:

• Machine Learning / AI Engineer building and deploying models in production.
• High Performance Computing Engineer or System Developer optimising scientific codes or enterprise applications for parallel architectures.
• Data Scientist working with large datasets and scalable pipelines.
• Software Engineer for cloud and distributed systems with a focus on performance and model serving.
• Researcher or PhD candidate in AI, computational science or related fields.

Alumni find employment across technology companies, research centres, finance, engineering and manufacturing firms, and in public research institutions. The programme’s combination of AI and HPC is particularly valuable for companies and labs working on simulation-driven design, computational biology, autonomous systems and large-scale data analytics.

Why study at University of Turin

The University of Turin offers a long-established academic environment with active research groups in artificial intelligence, data science and computational methods. Students benefit from hands-on laboratory teaching, opportunities to collaborate with local and national research institutes and industry partners, and access to computing resources through departmental clusters and wider national infrastructures.

Turin is a vibrant city with a strong industrial and technological ecosystem, providing internship and collaboration opportunities across automotive, aerospace, manufacturing and digital industries. The university also supports students with a range of academic services, seminar series, and career development activities to help transition into industry roles or further research.