Assistant Professor: Causal Inference & ML in Epidemiology

University of Pennsylvania; United (PA); On-site; USD 110,000 - 150,000; Full time

Job summary

University of Pennsylvania in Philadelphia seeks a Research Assistant Professor of Epidemiology in the non-tenure research track to advance causal inference and machine learning methods. A Ph.D.

or equivalent is required, with a strong record of peer-reviewed publications in related fields. The successful candidate will contribute to statistical programming (R, Python, SAS), reproducible research pipelines, and multi-site collaborations with Penn faculty and external partners.

Qualifications

• Ph.D. required in Biostatistics, Statistics, Epidemiologic Methods, or closely related field.
• Experience with causal inference and machine learning methods is required.
• Demonstrated record of first- or senior-authored peer-reviewed publications in related fields.

Responsibilities

• Lead development of causal inference methodologies for observational and experimental data.
• Publish high-impact research in biostatistics, epidemiology, and clinical research journals.
• Support grant submissions (e.g., NIH, PCORI, foundations) and multi-site collaborative projects.
• Mentor graduate students, postdocs, and junior investigators, and collaborate with clinician-scientists.

Skills

Causal inference

Machine learning

Biostatistics

Epidemiology

Education

Ph.D. or equivalent degree

Tools

R

Python

SAS

GitHub

Overleaf/LaTeX

Job description

University of Pennsylvania in Philadelphia seeks a Research Assistant Professor of Epidemiology in the non-tenure research track to advance causal inference and machine learning methods. A Ph.D.

or equivalent is required, with a strong record of peer-reviewed publications in related fields. The successful candidate will contribute to statistical programming (R, Python, SAS), reproducible research pipelines, and multi-site collaborations with Penn faculty and external partners.