Job Description

Postdoctoral Associate – Duke University School of Medicine Department of Obstetrics and Gynecology

Work Location: Durham, North Carolina or possibility for remote in the following states: California, Florida, Georgia, Maryland, New York, South Carolina, Tennessee, Texas, Virginia, Washington D.C.

Estimated Duration of Appointment: 48 Months

Position Summary

The Department of Obstetrics and Gynecology seeks a candidate with a doctoral degree in Epidemiology, Statistics, Health Services Research, or a related quantitative field. Our group uses mathematical modeling to explore and improve disparities in healthcare delivery and outcomes. The ideal candidate will have knowledge of natural history modeling and an interest in women’s health and healthcare.

The post-doc will have mentorship from an interdisciplinary team at Duke University. Drs. Havrilesky, Myers, and Ryser are currently collaborating on a newly funded U01 Cancer Intervention and Surveillance Modeling Network (CISNET) project whose aims are to improve outcomes and reduce disparities in uterine cancer prevention, detection and treatment ( .

The selected candidate will perform mathematical modeling of reproductive factors and the natural history of uterine cancer, as part of the NCI-funded U01 grant. Specifically, they will be responsible for obtaining model inputs from population-level surveys and databases, developing, maintaining and running mathematical models, and conducting a wide array of microsimulation experiments in support of the grant’s aims. They will prepare scientific manuscripts describing and implementing the proposed approach in applied settings, and present findings at conferences. The postdoctoral research associate will prepare material for discussion at weekly team meetings and monthly project-wide meetings, perform modeling, author related manuscripts, and participate in the CISNET modeling community as a junior investigator at national meetings.

Education and Experience Requirements: Completion of a PhD in Epidemiology, Statistics, Health Services Research, or a related quantitative field.

Required Qualifications, Competencies, and Experience: Experience with real-world (cancer) data and programming skills are required, including mastery of a low-level programming language such as R, Python, Julia, or C++. The position requires proficiency in at least a subset of the following: multistate disease modeling; statistical survival analysis, and simulation modeling. The successful applicant demonstrates great attention to detail, works independently, takes initiative, has excellent written and verbal communication skills, and performs well in an interdisciplinary environment at the interface between the quantitative modeling and medicine. The successful candidate is expected to have a track record of first-author publication in peer-reviewed journals.

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