Research Assistant Professor of Epidemiology

About the role

The Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine, University of Pennsylvania seeks candidates for several Assistant Professor positions in the non-tenure research track, focusing on causal inference and machine learning methods. The position is affiliated with the Center for Causal Inference (CCI) and the Center for Health Innovations in Reproductive and Perinatal Population Research (CHIRP) within the Division of Epidemiology.

Responsibilities

• Conduct research and scholarship requiring expertise in statistical programming (R, Python, SAS) and proficiency in analyzing and interpreting health outcomes.
• Demonstrate aptitude for working with state‑of‑the‑art computing infrastructure, Overleaf/LaTeX, GitHub, and reproducible research pipelines.
• Produce first‑ or senior‑authored peer‑reviewed publications in statistical methodology, causal inference, biostatistics, epidemiology, or related fields.
• Develop and evaluate novel statistical methods for observational and experimental data.
• Lead peer‑reviewed publications in causal inference methodology applied to perinatal health and support multi‑site collaborative research projects.
• Communicate complex quantitative methods to interdisciplinary scientific audiences.
• Collaborate with clinician‑scientists, mentor graduate students, postdoctoral fellows, or junior investigators.
• Contribute to federal and foundation grant submissions (e.g., NIH, PCORI, foundations).
• Support, prepare, and submit grant applications; support ongoing research studies with Penn faculty and external partners.

Qualifications

• Ph.D. or equivalent degree in Biostatistics, Statistics, Epidemiologic Methods, or a closely related quantitative field.
• At least 1+ years of postdoctoral experience.
• Demonstrated record of first‑ or senior‑authored peer‑reviewed publications in statistical methodology, causal inference, biostatistics, epidemiology, or related fields.
• Experience developing and evaluating novel statistical methods for observational and experimental data.
• Ability to lead peer‑reviewed publications in causal inference methodology applied to perinatal health.
• Experience collaborating with clinician‑scientists, mentoring trainees, and contributing to grant submissions.

Skills

• Statistical programming: R, Python, SAS.
• Proficiency in analyzing and interpreting health outcomes.
• Aptitude for state‑of‑the‑art computing infrastructure.
• Experience with Overleaf/LaTeX, GitHub, and reproducible research pipelines.

Applications will be reviewed beginning November 15, 2026; the position start date is flexible.