General Entrance Requirements

Applicants must satisfy the University's general entrance requirements and the following programme-specific entrance requirements:

• Applicants should possess a recognised undergraduate degree in Science / Bioengineering / Psychology / Medicine / Dentistry / Veterinary Medicine / Biostatistics / Computer Science or a related discipline.

• Preference for admission will be given to individuals who have completed a holistic undergraduate education with knowledge covering statistics, neuroscience, psychology, physiology, and bioengineering.

• Additional attention will be paid to students, who have little training in health-related area to ensure that they can follow the progress of the programme.

English Proficiency Requirements

Applicants whose entrance qualification is obtained from an institution where the medium of instruction is NOT English should also fulfill the following minimum English proficiency requirement:

• a score of 79 (for tests taken prior to 21 January 2026) or 4 (for tests taken from 21 January 2026 onwards) (Internet-based Test) in the Test of English as a Foreign Language (TOEFL)^@#; or

• an overall band score of 6.0 in International English Language Testing System (IELTS)^@#; or

• a score of 450 in band 6 of the Chinese mainland’s College English Test (CET6); or 

• other equivalent qualifications.

^@TOEFL and IELTS scores are considered valid for two years. Applicants are required to provide their English test results obtained within the two years preceding the start of the University's application period. 'TOEFL iBT Home Edition', ‘TOEFL MyBest Score’, ‘IELTS Indicator’, ‘IELTS One Skill Retake’ and ‘IELTS Online’ are not acceptable. All TOEFL results must be sent directly by the Educational Testing Service (ETS) using CityUHK’s institution code 3401, while IELTS results must be sent via the IELTS Results Service e-delivery to 'City University of Hong Kong - Graduate School'.

#Applicants with an IELTS overall band score of 6.0 or a TOEFL overall score of 4 will be required to pass an interview for English proficiency conducted by the concerned academic unit. Applicants with a comparable overall score of 79 or above shown on the TOEFL report may be exempt from the interview requirement. Interviews for other applicants may not be required.

Programme Aims

• Equip students with cutting‑edge knowledge and hands‑on skills in modern AI and brain‑inspired methods, including neural networks (e.g., CNNs, Transformers, GNNs), self‑supervised and contrastive learning, reinforcement learning, model interpretability, deployment and evaluation, integrated with computational neuroscience principles and data analysis.

• Develop the ability to critically evaluate limitations, biases, and safety issues in current AI systems and to propose and implement brain‑inspired algorithms and novel AI methodologies that address these challenges in neuroscience and healthcare applications (e.g., vision, decision‑making, BCI, clinical prediction).

• Prepare technical and non‑technical professionals to apply AI responsibly and ethically to real‑world problems across healthcare, technology, and society, with emphasis on fairness, accountability, transparency, and governance in biomedical and clinical contexts.

• Foster leadership, communication, and collaboration across disciplines by engaging students in research‑intensive, project‑based learning with real datasets, and by enabling effective translation of complex brain computing and AI concepts to diverse stakeholders in academia, industry, and clinical settings.

Programme Intended Learning Outcomes (PILOs)

• Understand how the anatomy and physiology of our nervous system enable the various functions such as learning and memory, sensory and motor function, and emotion.

• Understand how dysfunction of the nervous system can lead to neurological and psychiatric disorders, and how new therapeutics can be developed to treat them.

• Critically evaluate the latest research literature and apply it to provide solutions to current issues in nervous system health.

• Critically analyse, interpret, discuss and justify neuroscientific data.

• Communicate effectively, manage skillfully, and work closely with people from diverse education and professional background and experience. Communicate complex translational neuroscience concepts effectively to a variety of audience.

Students are required to complete 30 credit units for graduation from this Master of Science in Brain Computing and Artificial Intelligence (MSBCAI) programme.

Course Description

Core Courses (18 credit units)

Electives (12 credit units)

• Courses offered by different departments might have timetable conflicts, please plan ahead for the courses to register.

• *NS6001 Research Project in Neuroscience is typically completed for a maximum of two semesters (Semester A and Semester B). The enrolment quota is subject to the availability of the lab of the faculty members.

Department of Neuroscience (NS)
College of Biomedicine (CBM)
Department of Biomedical Sciences (BMS)
Department of Data Science (DS)
Department of Systems Engineering(SYE)