Post Doctoral Researcher

University of Pennsylvania: Postdoctoral Positions: Perelman School of Medicine Postdoctoral Location University of Pennsylvania - Perelman School of Medicine Open Date Jul 27, 2026 Description Faculty Mentor: Dr. Michael Harhay Department: Biostatistics, Epidemiology & Informatics Number of Positions: 1 Open to applications from U.S. citizens and foreign nationals The research groups of Drs. Scott Halpern, Michael Harhay, and Ari Friedman at the University of Pennsylvania invite applications for a postdoctoral researcher in health services research (HSR) and health economics. Individuals with >1 year of post-doctoral experience could be considered for research associate roles. We would be particularly interested in (bio)statisticians with training and interests in health economics and cost-effectiveness research. The postdoc researcher will support and lead projects using large, complex electronic health record (EHR) datasets, ranging from short, focused descriptive studies to natural experiments and causal inference. The position suits a candidate with prior experience using healthcare data (ideally including from electronic health records)who combines skills in a variety of econometric and statistical methods, technical sophistication in deploying those methodsrigorously, curiosity, andthe ability to work with clinicians and engage withclinical literatureto inform their work. The role places equal value on ambitious analysis and on the day-to-day discipline of data cleaning and documentation that underpins credible findings. The ideal candidate combines strong quantitative training with sound judgment about when data can and cannot answer a question. Working with colleagues fluent in both clinical practice and rigorous quantitative methods, the postdoc researcher will learn to distinguish mechanical features of healthcare data from substantive economic and meaningful findings and to exploit those features to strengthen causal inference. Research Activities The postdoc researcher will: Develop estimation strategies that use multiple sources of plausibly exogenous variation to identify clinically and economically relevant quantities of interest. Investigate natural experiments in the emergency department and ICU tied to fundamental economic questions, supported by additional clinical and operational data-gathering infrastructure. Explore distributed regression analyses across a coalition of health systems on emergency department boarding and crowding. Contribute statistical and cost-effectiveness expertise to ongoing and planned studies across the three research groups. Lead manuscript writing aimed at leading medical journals, in addition to health services research outlets. Data Stewardship and Documentation Clean and structure EHR data for analyses. Maintain careful and reproducible documentation of datasets/code and lead the creation of an organized catalog of existing datasets. Record methodological decisions in detail and use those notes to draft the Methods sections of manuscripts. Write data-sharing and replication plans. Prepare cuts of cleaned datasets for rotating students on an occasional basis. Team and Lab Coordination Serve as a point of contact between students in the lab and the faculty, assigning weekly tasks, monitoring progress, and supporting students' development. Maintain coding infrastructure for weekly reports tracking study progress and data completeness across active projects. Coordinate co-author feedback and track manuscript submissions. Develop and update training guides, study manuals, and materials supporting consistent research operations. Qualifications Doctoral degree in biostatistics, statistics, economics, epidemiology, health services research, or a related quantitative field, ideally with training or demonstrated interest in economics and cost-effectiveness analysis. Experience working with healthcare data and large relational datasets, with an understanding of the level of rigor the work requires. Proficiency in a statistical programming language such as R or Python. Skill in data cleaning and management, statistical and econometric analysis (including cost-effectiveness methods), manuscript writing and editing, research operations coordination, and supervision and mentorship of students. A view of data cleaning as essential to good research rather than as a chore. Ability to work well in teams and to manage time independently while coordinating among several cooperative principal investigators. A firm commitment to preserving the privacy of patient health information, including guarding against inadvertent disclosure within complex data-processing pipelines. 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. #J-18808-Ljbffr Upenn