Postdoctoral Fellow, Department of Diagnostic Radiology

Pok Fu Lam, Hong Kong
Friday, 18 September 2020
End of advertisement period
Friday, 16 October 2020
Contract Type
Fixed Term
Full Time

The University of Hong Kong 's highest priorities are to create opportunities for the very best academic talents to excel and to advance human knowledge to the benefit of society. We serve the needs of Hong Kong, the wider region and the rest of the world.

Work type: Full-time
Department: Department of Diagnostic Radiology (20500)
Categories: Academic-related Staff

Applications are invited for appointment as Post­-doctoral Fellow in developing deep learning and computational neuroscience methods for functional brain MRI analysis in the Department of Diagnostic Radiology (Ref: 502009), to commence as soon as possible for 15 months.   

Applicants should have a Ph.D. degree in Computer Science, Medical Physics, Biomedical Engineering, or related disciplines.  They should be self-driven, highly motivated, creative with excellent communication skills in written and spoken English.  They should also have experience in neuroscience, MRI, computer vision, and programming.  The research aims at building an A.I. software for computational neural science and functional MRI analysis.  The appointee will work in a multidisciplinary environment that requires exceptional research and problem-solving skills.  He/She will be provided with the opportunity on research training related to MRI data acquisition and to work on state-of-the-art deep learning techniques.  Enquiries about the post should be sent to Dr. Cao Peng at and Dr. Edward Hui at

A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.

The University only accepts online application for the above post.  Applicants should apply online and upload an up-to-date C.V.  Review of applications will start from October 5, 2020 and continue until October 16, 2020, or until the post is filled, whichever is earlier.

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