Postdoctoral Fellow in Deep Learning in Medical Image Analysis and Abdominopelvic Imaging

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

Applications are invited for appointment as Post­-doctoral Fellow in Deep Learning in Medical Image Analysis and Abdominopelvic Imaging in the Department of Diagnostic Radiology (Ref.: 494280), to commence as soon as possible for two years, with the possibility of renewal subject to satisfactory performance and funding availability.

The research group, led by Dr. Elaine Lee, is expanding the team with enthused scientists to work on the use of artificial intelligence and deep learning in all aspects of medical imaging and exploration of new imaging techniques in the field of abdominopelvic radiology.

Applicants should have a Ph.D. degree in Biomedical/Electrical Engineering, Computer Science, Physics, Biomedical Sciences, or a related field.  They should be self-driven, highly motivated, creative with excellent communication skills in written and spoken English; able to work in a dynamic, diverse and forward ­thinking department with an international working environment; and able to work independently and think “outside the box”.  They should also have first­hand experience in application of deep learning methods, especially in abdominopelvic imaging application.  Background in MR physics, sequence programming and/or image reconstruction algorithms will be favourable.  The appointee will work in close collaboration with radiologists, physicians, biomedical scientists and electrical engineers.  The Department has large amount of imaging data comprising of mainly CT, PET-CT and MRI with matching clinical data which can be used for retrospective projects.  Prospective and self-initiated projects will be encouraged.  Existing facilities include access to high computing GPU computers within the Department.  Enquiries about the post should be sent to Dr. Elaine Lee, Principal Investigator, 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. and a personal statement with research interest.  Review of application will start on April 1, 2019 and continue until April 30, 2019, or until the post is filled, whichever is earlier.

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