Research Assistant Professor of Epidemiology

About the role

The Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine, University of Pennsylvania, is seeking candidates for several Assistant Professor positions in the non‑tenure research track. The role focuses on causal inference and machine learning methods, with expectations to lead high‑impact research, develop novel statistical methods, support grant submissions, and collaborate across perinatal health and multi‑site projects.

Responsibilities

• Lead and publish first‑ or senior‑authored peer‑reviewed articles 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.
• Support multi‑site collaborative research projects.
• Effectively communicate complex quantitative methods to interdisciplinary scientific audiences.
• Collaborate with clinician‑scientists.
• Mentor graduate students, postdoctoral fellows, and 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.

Requirements

• Ph.D. or equivalent degree in Biostatistics, Statistics, Epidemiologic Methods, or a closely related quantitative field.
• At least 1 year of postdoctoral experience.
• Expertise in causal inference and machine learning methods.

Qualifications

• 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.
• Proficiency in statistical programming languages (R, Python, SAS).
• Ability to analyze and interpret health outcomes.
• Aptitude for working with state‑of‑the‑art computing infrastructure, Overleaf/LaTeX, GitHub, and reproducible research pipelines.
• Ability to lead peer‑reviewed publications in causal inference methodology applied to perinatal health.
• Experience collaborating with clinician‑scientists and mentoring trainees.
• Experience contributing to federal and foundation grant submissions.

Skills

• Statistical programming (R, Python, SAS).
• Data analysis and interpretation of health outcomes.
• Expertise in causal inference and machine learning.
• Scientific communication to interdisciplinary audiences.
• Collaboration and teamwork in multi‑site projects.
• Mentoring and training of junior researchers.
• Grant writing and submission (NIH, PCORI, foundations).
• Proficiency with Overleaf/LaTeX, GitHub, and reproducible research pipelines.