Research Assistant Professor of Epidemiology

Statistics Interest Group; Philadelphia (Philadelphia County); Hybrid; USD 110,000 - 160,000; Full time

Job summary

University of Pennsylvania: Perelman School of Medicine seeks candidates for Research Assistant Professor of Epidemiology in the Division of Biostatistics, Epidemiology, and Informatics. The role emphasizes advancing causal inference methodologies and developing novel statistical approaches for health outcomes.

The position requires a PhD in Biostatistics, Statistics, Epidemiologic Methods, or a closely related field, with 1+ year of postdoctoral experience.

Qualifications

• PhD in Biostatistics, Statistics, Epidemiologic Methods, or a closely related quantitative field.
• 1+ year of postdoctoral experience or equivalent.
• Expertise in causal inference and machine learning methods.

Responsibilities

• Advance causal inference methodologies and lead high-impact research in biostatistics and epidemiology.
• Publish in top-tier journals and contribute to federally funded grant submissions.
• Mentor graduate students, postdocs, and junior investigators; collaborate with clinicians and external partners.

Skills

Statistical programming

Causal inference

Biostatistics

Epidemiology

Grant writing

Mentoring

Education

PhD in Biostatistics, Statistics, Epidemiologic Methods, or related field

Tools

R

Python

SAS

Overleaf/LaTeX

GitHub

Job description

University of Pennsylvania: Perelman School of Medicine - Research Assistant Professor of Epidemiology

Company Name University of Pennsylvania: Perelman School of Medicine

Position Title Research Assistant Professor of Epidemiology

Company Information

The Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for several Assistant Professor positions in the non-tenure research track.

Duties and Responsibilities

Research or scholarship responsibilities may include expertise in statistical programming (R, Python, SAS) and proficiency in analyzing and interpreting health outcomes, along with a demonstrated aptitude for working with state-of-the-art computing infrastructure, Overleaf/LaTeX, GitHub, and reproducible research pipelines. The successful candidate will bring a demonstrated record of first- or senior-authored peer-reviewed publications in statistical methodology, causal inference, biostatistics, epidemiology, or related fields, as well as experience developing and evaluating novel statistical methods for observational and experimental data. The candidate should demonstrate the ability to lead peer-reviewed publications in causal inference methodology applied to perinatal health and support multi-site collaborative research projects, while effectively communicating complex quantitative methods to interdisciplinary scientific audiences. Additionally, experience in collaborating with clinician-scientists, mentoring graduate students, postdoctoral fellows, or junior investigators, and contributing to federal and foundation grant submissions (e.g., NIH, PCORI, foundations) is highly valued.

The Center for Causal Inference (CCI) and the Center for Health Innovations in Reproductive and Perinatal Population Research (CHIRP) in the Division of Epidemiology, Department of Biostatistics, Epidemiology, and Informatics seeks candidates with a PhD in Biostatistics, Statistics, Epidemiologic Methods, or a closely related quantitative field, with 1+ years of postdoctoral experience. The ideal candidates will be outstanding early-career researchers who will advance innovative causal inference methodologies and lead cutting-edge research in the development of novel causal inference and machine learning methods. This faculty member will be expected to lead and publish high-impact research in top-tier biostatistics, epidemiology, and clinical research journals; support, prepare, and submit grant applications; and support ongoing research studies with Penn faculty and external partners. We will begin reviewing applications on November 15th, 2026, and the position start date is flexible.

Position Qualifications

Expertise is required in the specific area of causal inference and machine learning methods. Applicants must have a Ph.D. or equivalent degree.