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US Food and Drug Administration

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FDA Fellowship in Regulatory Science Advancement for Continuous Glucose Monitoring (CGM) Endpoints U.S. Food and Drug Administration (FDA) in United States

Degree Level

Master's

Field of study

Meta-analysis

Funding

Monthly stipend commensurate with educational level and experience; renewal for a second year is contingent on the availability of funds.

Deadline

Nov 20, 2026

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Country

United States

University

US Food and Drug Administration

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Keywords

Meta-analysis
Mathematics
Medical Science
Salud Pública
Regulatory Science
Glucose Monitoring
Statistics
Statistical Modelling
Insulin

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About this position

This FDA fellowship is based in the Center for Drug Evaluation and Research and focuses on regulatory science for continuous glucose monitoring (CGM) endpoints in diabetes and related studies. Working with mentor Yoonhee Kim, the participant will help evaluate how CGM-derived measures such as Time in Range relate to hemoglobin A1c across multiple clinical trials and public datasets, with an emphasis on reproducible analyses that support regulatory decision-making.

The research includes two-stage meta-analytic and weighted least squares correlation methods to estimate surrogacy strength, compare CGM endpoints with evidentiary standards used in marketing approval, and build simulation datasets that reflect real-world data characteristics. The project also extends into modeling intermittent and informative missingness, hierarchical and temporal dependence in CGM data, and methods for complex outcomes such as time above/below target ranges, glycemic variability, and hypoglycemia event rates.

In addition, the fellow will contribute to the creation and refinement of structured prompts for FDA AI tools to automate routine CGM data cleaning, merging, and processing. The work is embedded in a regulatory environment that values clear documentation, auditability, and data governance, with opportunities to translate AI-assisted outputs into workflows suitable for regulatory review.

Funding details

Monthly stipend commensurate with educational level and experience; renewal for a second year is contingent on the availability of funds.

What's required

The qualified candidate should be currently pursuing or have received a master's or doctoral degree in a relevant field. Preferred qualifications include experience with surrogacy analyses using regression and/or meta-analytic models including Bayesian methods, knowledge of continuous glucose monitoring data or other digitally derived endpoint data, programming experience using SAS and R for large datasets, and ability to construct and execute simulation datasets grounded in real data. Strong communication, presentation, and critical thinking skills are preferred. Applicants must be U.S. citizens, Lawful Permanent Residents, or foreign nationals who meet the program's immigration requirements; proof of health insurance is required, and a successful background investigation is required for onboarding.

How to apply

Scroll to the bottom of the opportunity page and click APPLY. Submit an application, transcripts, a current resume/CV, and one educational or professional recommendation. Send questions to [email protected] and include the reference code FDA-CDER-2026-0181.

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