Research Assistant Professor of Epidemiology

University of Pennsylvania Perelman School of Medicine; Philadelphia (Philadelphia County); On-site; USD 95,000 - 130,000; Full time

Job summary

The University of Pennsylvania's Perelman School of Medicine seeks applicants for several Assistant Professor positions in the non-tenure research track in the Department of Biostatistics, Epidemiology, and Informatics. Expertise in causal inference and machine learning is required, and a PhD or equivalent is essential.

Responsibilities include leading methodological research, publishing, grant submissions, mentoring trainees, and collaborating with clinicians on interdisciplinary studies.

Qualifications

• PhD or equivalent degree is required.
• Expertise in causal inference and machine learning methods.
• Ability to lead first- or senior-authored publications in relevant fields.

Responsibilities

• Develop novel causal inference and ML methodologies for health data.
• Publish research in top biostatistics and epidemiology journals and lead grant submissions.
• Mentor graduate students, postdocs, and junior investigators; collaborate with clinicians.

Skills

Causal inference

Machine learning

Statistical programming

Education

PhD in Biostatistics/Statistics/Epidemiologic Methods or related

Tools

R

Python

SAS

Job description

University of Pennsylvania: Perelman School of Medicine: Perelman School of Medicine - Department of Biostatistics, Epidemiology and Informatics

Location

Philadelphia, PA

Open Date

Sep 25, 2026

Deadline

Sep 25, 2028 at 11:59 PM Eastern Time

Description

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

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.