Postdoc Research Fellow Pepin Lab

The Pepin lab studies female reproductive development and diseases of the reproductive system, such as infertility, and ovarian cancer. The Pepin Lab is partnering with MIT's Female Medicine through Machine Learning (FMML), to help develop individualized medicine for women by leveraging artificial intelligence (AI) and real-world health data to address gaps in female health research and care. Our mission is to transform disease discovery, detection, and delivery for women by developing open-source, AI-driven solutions using diverse medical datasets, including electronic health records (EHRs) and large biobanks such as UK Biobank and MGB Biobank.

We seek a highly motivated Postdoctoral Fellow with demonstrated experience in machine learning applied to electronic health records and large-scale medical datasets (e.g., UK Biobank, MGB Biobank). The successful candidate will contribute to FMML's mission by developing and applying advanced AI methods to uncover novel insights in female health, create disease detection tools, and launch impactful clinical applications.

The Postdoctoral Fellow will be expected to develop research methodologies to allow quantitative and qualitative evaluation and interpretation of data obtained. Participate in the interpretation of the results of experiments through conferencing with other senior lab personnel or principal investigator to review data compared to hypothesis, and researches methodology in instances of inexplicable data. Collaborates with principal investigator in writing material for publication;

may present papers or appear as principal or secondary author in publications and ensures Public Access policy is adhered to.

Duties will be performed in a research laboratory. This setting may create contact or exposure to one or more of the following: chemicals, fluids, sharp instruments, and other supplies and equipment consistent with a research lab. Contact or exposure may be airborne or physical. Work schedule may be flexible and may require occasional evening, weekend, or holiday hours.

• Design and implement machine learning models for analyzing EHRs and biobank data, with a focus on sex‑specific health outcomes and under explored conditions in women.
• Develop predictive models and biomarkers (e.g., ovarian aging, endometriosis and PCOS early detection) using multi‑modal, longitudinal, and real‑world data.
• Collaborate with a multidisciplinary team of clinicians, data scientists, and faculty to translate research into clinical tools and open‑source resources.
• Aggregate and analyze literature on sex differences in health to inform model development and support the creation of a comprehensive canon of female medicine.
• Prepare and submit research manuscripts for peer‑reviewed publication and present findings at scientific conferences.
• Mentor junior researchers and contribute to grant applications as needed.
• Attend and may make presentations at lab meetings.
• Structure lab operations; prioritize and assign work to lower level personnel; monitor quality and quantity of work performed and ensure standards are met and maintained.
• Interpret the results of experiments through conferencing with senior lab personnel or principal investigator to review data compared to hypothesis, and research methodology in instances of inexplicable data. Organize and summarize acquired data, using scientific and statistical techniques.
• Collaborate with principal investigator in writing material for publication; may present papers or appear as principal or secondary author in publications and ensure Public Access policy is adhered to.
• May teach moderately difficult-to-complex analyses to students and research personnel.
• May provide functional guidance to personnel and trainees.
• Prudent use of hospital resources expected.
• Perform other duties as assigned.

• Ph.D. (or equivalent) in biomedical informatics, computer science, statistics, computational biology, or a related field.
• Hands‑on experience with machine learning and deep learning applied to EHRs and large medical datasets (e.g., UK Biobank, MGB Biobank).
• Proficiency in programming…