Master of Science in Materials Intelligence (This programme is offered subject to approval)
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Entrance Year Sept 2027
Programme Code 46014
Stream Code
MI (Full-time)
MIT (Part-time)
Mode of Study Mixed Mode
Normal Duration
1.5 years (Full-time)
2.5 years (Part-time)
Fund Type Self-Financed
Credits Required for Graduation
31
Local Application Deadline
25 Feb 2027 *
Early Round: 2026-10-20
Main Round: 2027-02-25
Non-Local Application Deadline
25 Feb 2027 *
Early Round: 2026-10-20
Main Round: 2027-02-25
Tuition Fees
TBC
Programme Leader(s)
Prof. ZHAO Haitao
PhD, MSc, BEng
Remarks
Note to Applicants
The programme has a limited quota for admission. Early application is strongly encouraged.
Aims and Characteristics
Programme Aims
The Master of Science in Materials Intelligence (MSc in MI) programme aims to achieve the following:
To provide students with a comprehensive interdisciplinary foundation in Materials Intelligence, covering AI for Science and AI + Materials research, as well as applications of Embodied AI and Physical AI.
To equip students to use AI, robotics, large language models, digital twins and AI agents to accelerate the design, discovery, synthesis and manufacturing of new materials.
To enable students to understand and develop advanced materials for AI and robotics industries, including next-generation robotic infrastructure, AI chips, renewable energies and emerging space manufacturing.
Characteristics
The MSc in Materials Intelligence offers interdisciplinary training across advanced materials, artificial intelligence, and robotics, with exposure to AI for Science, Embodied AI, and Physical AI. Students will learn through research-informed teaching, practical projects and engagement with leading universities, research institutions and listed companies. Planned collaborations with leading universities and research institutions, such as the University of Oxford and Suzhou National Laboratory, will provide opportunities for guest lectures and research exposure. Students may also access internship opportunities in Suzhou and Wenzhou. Such experiences can help students connect academic knowledge with research, industrial applications and innovation, supporting future careers in AI, digital twins, robotics and new materials.
