Biostatistics Associate or Full Professor, Tenure Track or Clinician-Educator

The Department of Biostatistics, Epidemiology and Informatics (DBEI) at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for several Associate or Full Professor positions in either the non-tenure clinician educator track or the tenure track. Expertise is required in the specific area of biostatistical methods development and applications. Applicants must have a Ph.D. or equivalent degree.

Tenure track applicants will focus primarily on the development of innovative leading-edge statistical methodology, with secondary emphasis on collaborative research projects within the Perelman School of Medicine. With respect to methodology development, an established track record is required, along with the ability to establish oneself as a research leader in their area of specialization. It is required that the applicant has served as Principal Investigator of methodological research supported by extramural grant funding.

Clinician-Educator track applicants who have outstanding productivity in collaborative research, as well as some methodological research, are especially encouraged to apply. There is a rich mix of ongoing biomedical research programs in the Perelman School of Medicine to provide motivation and opportunities for the development of novel statistical methods on wide-ranging domains of biomedical, clinical, and translational science.

Teaching responsibilities may include participation in biostatistics PhD and MS training programs, as well as teaching biostatistics courses in epidemiology, health policy, and other biomedical programs. Candidates are expected to have a strong commitment to teaching and mentoring.

Research or scholarship responsibilities may include statistical methods development and contributions as a statistical leader for collaborative research studies. Candidates with experience and/or an interest in cancer clinical trials, pediatric research, the interface between biostatistics and artificial intelligence, and causal inference are especially encouraged to apply.