THE UNIVERSITY OF HONG KONG

Post­doctoral Fellow, Department of Diagnostic Radiology

Location
Pok Fu Lam, Hong Kong
Posted
Thursday, 4 June 2020
End of advertisement period
Wednesday, 29 July 2020
Ref
500306
Contract Type
Fixed Term
Hours
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.

Developing Deep Learning Method for Prostate Cancer Detection and Grading in MRI

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 Method for Prostate Cancer Detection and Grading in MRI in the Department of Diagnostic Radiology (Ref.: 500306), to commence as soon as possible for one year, with the possibility of renewal subject to satisfactory performance and funding availability.   

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.  The research aims at building an A.I. software for prostate cancer detection and grading in MRI.  Applicants should have experience in MRI, computer vision, and programming.  The appointee will work in a multidisciplinary environment that requires exceptional research and problem-solving skills.  Research training on MRI data acquisition will be provided.  He/She will also be provided with the opportunity to work on state-of-the-art deep learning techniques.  Inquiries about the post can be sent to Dr. Cao Peng at caopeng1@hku.hk and Dr. Vince Vardhanabhuti at varv@hku.hk.

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 June 17, 2020 and continue until July 29, 2020, or until the post is filled, whichever is earlier.

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