Postdoctoral Fellow in the Harvard-MIT Center for Regulatory Science

Position Description

We invite applicants for a postdoctoral fellow position in the Harvard-MIT Center for Regulatory Science at Harvard Medical School in Boston, Massachusetts. The work of the Center, directed by Dr. Florence Bourgeois, is focused on the regulation of medical products by the U.S. Food and Drug Administration (FDA) and the evidence used to support regulatory decisions. Through its Clinical Evidence Evaluation and Communication Unit, the Center conducts empirical and policy-relevant research on the strength of the clinical evidence underlying drug and biologic approvals, the design and completion of FDA-required postmarket studies, and how information about benefits, harms, and uncertainty is communicated to clinicians, patients, and payers. We welcome applications from recent PhD graduates and postdoctoral fellows who are interested in these or related fields, particularly those who may bring a new technology, method, or perspective to bear on the work of the Center. We are especially interested in candidates with strong quantitative, data-analytic, and writing skills who are eager to apply these abilities to questions in regulatory science, pharmaceutical policy, and the evidentiary basis of medical product regulation.

The successful candidate will work with Dr. Florence Bourgeois and Unit collaborators to develop an independent research project within the scope of the Center's research program. In addition to carrying out data-driven and policy-relevant research, the successful candidate will be expected to contribute to the writing of grants and manuscripts, participate in mentoring of team members as needed, and otherwise contribute to overall Unit operations and a collaborative, interdisciplinary research environment.

Basic Qualifications

Ph.D. or M.D./Ph.D. in areas such as health policy, health services research, epidemiology, biostatistics, data science, economics, pharmacy, medicine, or related fields
Strong quantitative and programming skills, including proficiency in R, Python, or similar languages, and experience working with large or complex datasets
Interest or experience in regulatory science, pharmaceutical or health policy, clinical evidence evaluation, or the analysis of regulatory and clinical documents; familiarity with large language models or natural language processing is a plus

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