Publisher
source

US Food and Drug Administration

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FDA Fellowship in Statistical Methods for New Approach Methodologies (NAMs) U.S. Food and Drug Administration (FDA) in United States

Degree Level

Master's

Field of study

Computer Science

Funding

Monthly stipend commensurate with educational level and experience; appointment initially for one year and may be renewed contingent on availability of funds.

Deadline

Aug 31, 2027

Country flag

Country

United States

University

US Food and Drug Administration

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Keywords

Computer Science
Biology
Mathematics
Predictive Modeling
Risk Assessment
Artificial Intelligence
Medical Science
Pharmacy
Salud Pública
R Programming
Statistics
ML

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About this position

This research fellowship at the U.S. Food and Drug Administration, Center for Drug Evaluation and Research (CDER), is based in Silver Spring, Maryland and focuses on statistical methods for New Approach Methodologies (NAMs). The project sits at the intersection of statistical science, in vitro pharmacology, and regulatory decision-making, with direct relevance to FDA’s work on drug safety and evaluation.

The fellow will contribute to interrelated research projects developing and assessing statistical frameworks for regulatory use of NAMs. A major theme is the analysis of dose-response data, including methods for evaluating the parallelism of test and reference curves through simulation studies. The work also includes comparing modeling approaches for high-dimensional in vitro datasets, such as logistic regression, principal component analysis, and artificial neural networks, to identify effective frameworks for complex assay data.

Another important part of the fellowship is building a statistical framework for proarrhythmia risk assessment. This includes using in vitro assay data from CiPA reference compounds to study cardiac safety signals such as delayed repolarization and QT interval prolongation, with the goal of supporting regulatory decisions under ICH S7B/E14 guidelines.

The fellowship offers experience in applied statistical programming, machine learning workflows, uncertainty quantification, quantitative risk assessment, and reproducible analysis for multidisciplinary scientific and regulatory audiences. The mentor for the opportunity is Meiyu Shen at FDA CDER.

Funding details

Monthly stipend commensurate with educational level and experience; appointment initially for one year and may be renewed contingent on availability of funds.

What's required

The qualified candidate should be currently pursuing or have received a master's degree in a relevant field. Citizenship is restricted to U.S. citizens and Lawful Permanent Residents (LPRs). Applicants must be able to provide transcripts, a current resume/CV, and one educational or professional recommendation, with all documents in English or accompanied by an official English translation. Proof of health insurance is required. A successful background investigation by the Office of Personnel Management is required, and the applicant must have resided in the United States for a total of three of the past five years to be eligible for onboarding at FDA.

How to apply

Submit the application through the APPLY button on the opportunity page. Include transcripts, a current resume/CV, and one recommendation; all documents must be in English or include an official English translation. For questions, email [email protected] and include the reference code FDA-CDER-2026-0144.

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