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University of Pennsylvania
Philadelphia, Pennsylvania, United States
(on-site)
Posted
25 days ago
University of Pennsylvania
Philadelphia, Pennsylvania, United States
(on-site)
Job Type
Full-Time
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Other
Causal Inference Postdoctoral Researcher - Center for Causal Inference, University of Pennsylvania
The insights provided are generated by AI and may contain inaccuracies. Please independently verify any critical information before relying on it.
Causal Inference Postdoctoral Researcher - Center for Causal Inference, University of Pennsylvania
The insights provided are generated by AI and may contain inaccuracies. Please independently verify any critical information before relying on it.
Description

Causal Inference Postdoctoral Researcher - Center for Causal Inference, University of Pennsylvania
Location: University of Pennsylvania - School of Medicine
Open Date: Dec 02, 2025
Deadline:
Faculty Mentors: Dr. Eric Tchetgen Tchetgen and Dr. Nandita Mitra.
Department: The Center for Causal Inference (CCI) in the Department of Biostatistics, Epidemiology & Informatics (DBEI)
This position is open to applications from US citizens and foreign nationals.
The Center for Causal Inference (CCI) is a research center in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine of the University of Pennsylvania (https://www.dbeicoe.med.upenn.edu/cci/about-us) with close collaboration with the Wharton Statistics Department. The CCI is a multidisciplinary center that includes faculty, postdocs, and graduate students from Biostatistics, Statistics, Epidemiology, Criminology, Computer Science, Philosophy, and other disciplines. The mission of the CCI is to be the leading center for research and training in the development and application of causal inference theory and methods, with far-ranging applications in Health and Social Sciences.
The position is for a postdoctoral researcher in causal inference with considerable flexibility in terms of research focus, reflecting the interdisciplinary nature of the field. In particular, the postdoctoral researcher will have an opportunity to collaborate on a broad range of methodological projects under the mentorship of one or more faculty affiliated with CCI.
Sample content areas include:
- Developing methods to study the effect of antidiabetic treatments on the recurrence of hospitalizations for T2DM-related complications.
- Developing methods for constructing just-in-time adaptive interventions using mobile health (mHealth) data.
- Developing novel causal inference methods to reconcile possibly conflicting results between randomized trials or randomized trials and observational studies, particularly in the area of pregnancy research.
In addition, the researcher will receive mentorship for professional development from the CCI co-directors Professors Nandita Mitra and Eric Tchetgen Tchetgen, as well as dedicated mentorship based on project type, with the expectation that their time as a researcher will advance their career development.
Duties and Responsibilities: develop new theory and methods in causal inference; develop software; and potentially collaborate on applied projects in Health and Social Sciences. In addition, the fellow is expected to be an active member of the CCI, by, for example, participating in the weekly working group meeting and the annual Causal Inference Summer Institute.
Qualifications
Candidates should have a doctoral degree in biostatistics, statistics, or a related field. Strong computational skills and expertise in causal inference are desired but not required. Start date and term are negotiable.
Application Instructions
Please include a cover letter, a curriculum vitae, a research statement (2 pages maximum) and three references contact information.
To apply, visit https://apply.interfolio.com/178517
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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Job ID: 84367706
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