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Research Associate – Causal

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Onsite

Research Associate – Causal

University of Pennsylvania

Research Associate – Causal

Skills

Machine Learning

6 months ago

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Important information


Contract type:

Permanent

Location:

Philadelphia, PA, USA

Work mode:

Onsite

Published on:

7 August 2026

What they need


The Department of Biostatistics and Epidemiology at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for several Research Associate positions in the Academic Support Staff. This appointment will be initially for one (1) year and continuation during that time period and renewal are based on satisfactory performance and availability of funding (limited to three (3) years). Applicants must have a Ph.D. or equivalent degree.

Responsibilities may include : (1) development and application of statistical and machine learning methods for analyzing electronic health records (EHRs) data, claims data, and biobank data; (2) manage the development and documentation and technical support of industry grade open access software implementing state-of-the-art causal inference methods developed by researchers at the Penn Center for Causal Inference; and (3) collaboration on cutting-edge translational biomedical research with CCI trainees and other researchers working with the Penn School of Medicine and Children’s Hospital of Philadelphia.

The successful applicant will have an opportunity to work under the supervision of PI Dr. Eric Tchetgen Tchetgen, with a focus on methodological developments for Causal Inference and related Missing Data Problems. The candidate is expected to build a clear path towards an independent researcher in preparation for the next stage of their career.

Equal Employment Opportunity Statement The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.

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