- Qualification PhD
- Apply Before Open Until Filled
- Discipline Science & Engineering, Art, Law, Political Science, Education, Literature, Medical & Health Science
- Last Refreshed December 18, 2021
Applications are invited for a number of positions as Post-doctoral Fellow (PDF) at the University of Hong Kong. Appointments will be made for a period of 2 to 3 years and the appointees must be in post on or before April 30, 2022.
PDF posts are created specifically to bring new impetus and vigour to the University’s research enterprise. Positions are available from time to time to meet the strategic research needs identified by the University. Positions are available in the following Faculties/Departments/Schools/Laboratories:
PDFs are expected to devote full-time to research. Applicants should be doctoral degree holders having undertaken original research that has contributed to the body of knowledge. A highly competitive salary commensurate with qualifications and experience will be offered. Annual leave and medical benefits will also be available.
Prospective applicants are invited to view the list of the Faculties / Departments / Schools / Laboratories and their research areas in which PDF positions are currently available, and apply online by clicking the respective opening below. Before preparing an application, they should contact the Head of the respective academic unit, or the contact person as specified, to ascertain that their research expertise matches the research area in which a vacant PDF post is available.
|503922||Faculty of Dentistry||Biomedical Engineering and Biofunctional Materials|
|504003||Faculty of Education||Early Childhood Development and Education|
|508757||Faculty of Education||Educational Neuroscience & Machine Learning|
|503914||Electrical and Electronic Engineering||Neuromorphic Computing with Emerging Nanoelectronics Devices|
|508268||Biomedical Sciences||Cancer Signaling and Drug Targets|
|503859||Public Health||Novel Drug Targets for Global Healthy Aging Promotion from the Evolutionary Public Health Perspectives|
|503908||Chemistry||Machine Learning in Computational Chemistry|