Assistant Professor of Causal Inference & ML in Epidemiology

University of Pennsylvania; Philadelphia (Philadelphia County); On-site; USD 110,000 - 160,000; Full time

Job summary

The Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for Assistant Professor roles in the non-tenure research track. Expertise in causal inference and machine learning methods is required, with a PhD or equivalent degree.

Successful candidates will conduct innovative methodological research, publish in top journals, and contribute to grant submissions while collaborating with Penn faculty and external

Qualifications

• PhD in Biostatistics, Statistics, Epidemiologic Methods, or closely related quantitative field.
• Strong background in causal inference and machine learning for health data.
• Proven ability to publish peer reviewed methodological work.

Responsibilities

• Develop and evaluate novel causal inference and ML methods for observational data.
• Lead high-impact publications and present results to interdisciplinary audiences.
• Collaborate across Penn centers and external partners on grant-funded studies.
• Mentor graduate students, postdocs, and junior investigators.

Skills

Causal inference

Machine learning

Statistical programming

Collaboration

Mentoring

Education

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

Tools

R

Python

SAS

Job description

The Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for Assistant Professor roles in the non-tenure research track. Expertise in causal inference and machine learning methods is required, with a PhD or equivalent degree.

Successful candidates will conduct innovative methodological research, publish in top journals, and contribute to grant submissions while collaborating with Penn faculty and external