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University of Pennsylvania invites applications for a Research Assistant Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics. The role focuses on biomedical informatics methods development and applications, with emphasis on NLP, AI/ML, bioinformatics and translational informatics. Candidates should have a Ph.D.
and a track record of scholarly work. The position is non-tenure, research-track and expects external funding, collaboration with a standing faculty
Location: Philadelphia, PA
Open Date: Oct 01, 2026
Deadline: Oct 01, 2028 at 11:59 PM Eastern Time
The Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for an Assistant Professor position in the non-tenure research track. Expertise is required in the specific area of biomedical informatics methods development and applications. Candidates with postdoctoral training are encouraged to apply. Applicants must have a Ph.D. or equivalent degree.
Research or scholarship responsibilities may include conducting research with a standing faculty member involving collaborative research studies, and the development, evaluation, or application of biomedical informatics methods. Candidates with experience in NLP, AI and Machine Learning, bioinformatics, translational bioinformatics, clinical informatics, clinical research informatics, consumer health informatics, and/or public health informatics are encouraged to apply.
Research track faculty typically work on extramurally-funded research within a research group led by a standing faculty member. The successful candidate will be expected to attract and maintain extramural funding. They should have experience conducting research and publishing peer reviewed manuscripts.
Equal Employment Opportunity Statement The University of Pennsylvania is an equal opportunity employer.Candidates are considered for employment without regard to race,color, sex, sexual orientation, religion, creed, national origin(including shared ancestry or ethnic characteristics), citizenshipstatus, age, disability, veteran status or any class protectedunder applicable federal, state, or local law.
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