FDA Pediatric Extrapolation: Analysis Using FDA Clinical Trial Data

U.S. Food and Drug Administration (FDA)

FDA-CDER-

12/11/2026 3:00:00 PM Eastern Time Zone

• Applications will be reviewed on a rolling-basis.

A research opportunity is available at the U.S. Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) Office of Rare Diseases, Pediatrics, Urologic and Reproductive Medicine (ORPURM), in the Division of Pediatrics and Maternal Health (DPMH) located in Silver Spring, Maryland. The participation is hybrid with some in-person at the FDA White Oak Campus in Silver Spring, Maryland.

Pediatric extrapolation relies on the use of inferences about efficacy in children often based on data in adults based on assumptions or evidence of similarity in disease, drug pharmacology and treatment response between adults and children. The need to systematically assess the assumptions underlying extrapolation is well recognized in pediatric drug development. The FDA has access to data from approximately 1,200 pediatric clinical trials, including data from 600 trials spanning more than 300 pediatric development programs, which enables a systematic, disease-focused review.

The proposed project aims to analyze adult and pediatric clinical trial data to evaluate the assumptions underlying the extrapolation of pharmacokinetics (PK), efficacy, and safety for selected diseases/conditions.

You will participate in activities related to the research project, including data collection from public and proprietary sources, data analysis and interpretation, and preparation and presentation of research findings for scientific dissemination and publication.

Under the guidance of the mentor(s), you will be involved in a systematic review of adult and pediatric clinical trial data, assess similarities of disease between adults and children; understand expected treatment response differences in children, if any; you will assess possibility of leveraging information from previous studies (adult and pediatric) and provide a framework for optimizing trials.

The mentor for this opportunity is Lily (Yeruk) Mulugeta (yeruk.mulugeta.gov). If you have questions about the nature of the research, please contact the mentor.

January 2027. Start date is flexible and will depend on a variety of factors.

The appointment will initially be for one year, but may be renewed upon recommendation of FDA and is contingent on the availability of funds.

The participation is hybrid with some in-person at the FDA White Oak Campus in Silver Spring, Maryland.

The participant will receive a monthly stipend commensurate with educational level and experience.

This opportunity is available to U.S. citizens and Lawful Permanent Residents (LPR) only.

This program, administered by ORAU through its contract with the U.S. Department of Energy to manage the Oak Ridge Institute for Science and Education, was established through an interagency agreement between DOE and FDA. The participant will receive a monthly stipend commensurate with educational level and experience. Proof of health insurance is required for participation in this program. Participants do not become employees of FDA, DOE or the program administrator, and there are no employment-related benefits.

Completion of a successful background investigation by the Office of Personnel Management is required for an applicant to be on-boarded  can complete a background investigation only for individuals, including non-US Citizens, who have resided in the US for a total of three of the past five years.

The qualified candidate should be currently pursuing or have received a Master's or Doctoral degree in one of the relevant fields. Degree must have been received within five years of the appointment start date, or be currently pursuing.

• Citizenship: LPR or U.S. Citizen
• Degree:
  Master's Degree or Doctoral Degree received within the last 60 months or currently pursuing.
• Discipline(s):
  • Computer, Information, and Data Sciences (Artificial Intelligence (including Robotics, Computer Vision, Human Language Processing, and Machine Learning)))
  • Computer Architecture and Grids
  • Computer Science
    - Languages and Systems
  • Computer Science
    - Theoretical…