Applications are invited for appointment as Research Assistant Professor in the School of Public Health (Ref.: 519617), to commence as soon as possible on a two-year fixed-term basis, with the possibility of renewal subject to funding availability and satisfactory performance.
Applicants should possess a Ph.D. degree in data science, bioinformatics, pharmacology, public health or a related discipline. They should have solid research experience in computational medicine, especially in designing and developing deep learning models for drug discovery and disease progression. A track record of high-quality impactful research demonstrated by publications and external grants is also required. They must have solid knowledge of AI frameworks including PyTorch and TensorFlow, and excellent programming skills in Python and R. They should have an excellent command of written and spoken English and Chinese. They should be organized, responsible, detail-minded, self-motivated, and able to work independently as well as in a multidisciplinary team.
The appointee will lead research projects about developing interpretable deep learning models to enhance medication effectiveness and safety, using various datasets such as genomics, proteomics and electronic medical records. He/She will supervise junior research staff and students; develop his/her own research programme; write scientific publications, grant proposals and reports; and perform other duties as assigned. Enquiries about the duties of the post should be sent to Dr. Kathy Leung at email@example.com. Information about the School can be obtained at http://sph.hku.hk.
A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. At current rates, salaries tax does not exceed 15% of gross income. The appointment will attract a contract-end gratuity and University contribution to a retirement benefits scheme, totalling up to 15% of basic salary.
The University only accept online application for the post. Applicants should apply online and upload an up-to-date C.V. and research publication list. Review of applications will commence on March 20, 2023 and continue until August 31, 2023, or until the post is filled, whichever is earlier.