Post­doctoral Fellow in Developing Deep Learning Method for Prostate Cancer Detection and Grading in MRI
University of Hong Kong
Hong Kong

Post­-doctoral Fellow in Developing Deep Learning Method for Prostate Cancer Detection and Grading in MRI

The University of Hong Kong

Ref.: 500306

Work type: Full-time

Department: Department of Diagnostic Radiology (20500)

Categories: Academic-related Staff
Hong Kong

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

Advertised:Jun 3, 2020 (HK Time)

Applications close:Jul 29, 2020 (HK Time)

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