University of Siena

What you'll study

The programme builds a balanced curriculum of theoretical foundations, applied methods and hands-on laboratory work. Students take advanced modules in machine learning, deep learning, probabilistic modelling and optimisation alongside courses in automatic control, robotics, mechatronics and industrial automation. Core topics typically include:

• Machine learning and statistical methods — supervised and unsupervised learning, probabilistic graphical models, reinforcement learning.
• Deep learning and computer vision — neural networks, convolutional architectures, image and video analysis.
• Natural language processing and knowledge representation — language models, symbolic and hybrid AI approaches.
• Control theory and automation — linear and non‑linear control, real‑time control systems, PID and model predictive control.
• Robotics and embedded systems — robot kinematics and dynamics, sensor integration, real‑time embedded architectures.
• Industrial systems and Industry 4.0 — PLCs, SCADA, factory automation, cyber‑physical systems and IoT platforms.
• Software engineering for AI systems — scalable architectures, cloud and edge deployment, validation and testing of AI solutions.
• Ethics, safety and regulation — aspects of AI safety, ethics of autonomous systems and relevant regulatory frameworks.

Teaching methods combine lectures, computer and robotics laboratories, group design projects and seminars. Students complete a substantial research or applied project (thesis) in their final year, often done in collaboration with research groups or local industry partners. Opportunities for internships, exchange semesters and participation in multidisciplinary initiatives are integrated into the programme.

Entry requirements

Applicants are normally expected to hold a recognised bachelor’s degree in engineering, computer science, mathematics, physics or an equivalent qualification with a strong quantitative background. Typical entry requirements include:

• A first degree providing knowledge of calculus, linear algebra, probability and basic programming.
• Prior exposure to programming (for example Python, C/C++ or MATLAB) and basic algorithms and data structures.
• A statement of purpose outlining relevant experience and intended study or research goals.
• Academic transcripts and, where applicable, letters of recommendation.
• Proof of language proficiency if instruction is in a language other than the applicant's native language.

Deficiencies in specific areas can often be remedied by taking preparatory courses before or during the early part of the programme. International applicants should consult the programme admissions office for precise documentation and credential evaluation guidance.

Career prospects

Graduates are equipped for roles across technology, manufacturing and research sectors. Common career paths include:

• AI engineer / machine learning engineer — developing and deploying models for products and services.
• Automation and control systems engineer — designing and maintaining industrial control systems and PLC‑based solutions.
• Robotics engineer — working on robot design, navigation, perception and manipulation systems.
• Data scientist / analytics specialist — extracting insight from large data sets and building predictive systems.
• Systems integrator and Industry 4.0 consultant — implementing cyber‑physical and smart manufacturing solutions.
• Researcher or PhD candidate — pursuing advanced research in universities, public research institutes or corporate R&D labs.

Graduates find employment in a variety of organisations including industrial manufacturers, automation and robotics companies, software and IT firms, research centres and start‑ups. The combination of AI and automation skills is also valuable for entrepreneurial projects in intelligent systems and smart services.

Why study at University of Siena

The University of Siena offers a focused environment combining strong academic teaching with applied research in information engineering and automation. The programme benefits from the university's research groups working on machine learning, computer vision, control systems and robotics, and provides access to dedicated laboratories and testbeds.

• Applied research links — active collaboration between departments and with regional industry supports project work and internships.
• Practical training — laboratory-based courses and real‑world projects prepare students for industrial deployment of AI and automation solutions.
• Interdisciplinary approach — links with computer science, mathematics and engineering departments encourage interdisciplinary problem solving.
• Location and industry access — situated in Tuscany, the university provides access to regional technology companies and a network of SMEs engaged in automation and advanced manufacturing.

Overall, the programme is designed to deliver both the theoretical foundations and practical competencies needed to design, build and evaluate intelligent automated systems for research and industry.