FDA Computational Biology and Pharmacology Fellowship

About the Opportunity

Under the guidance of a mentor, you will help develop comprehensive toxicology databases and computational models by analyzing proprietary data from drug applications and public literature. The focus is on endpoints where New Approach Methodologies (NAMs) can provide significant regulatory value, integrating machine learning, statistical analysis, and mechanistic modeling to translate nonclinical data into safety endpoints such as cardiotoxicity and immunogenicity.

• Collaborate with multidisciplinary teams to curate large‑scale datasets.
• Investigate patterns in toxicological data using statistical methods.
• Contribute to the development of reference compound libraries for predictive model validation.
• Develop AI, statistical, or mechanistic models to translate nonclinical data into safety endpoints.
• Participate in stakeholder engagement activities with industry and academic partners.
• Maintain data management systems and quality control procedures.

• Currently pursuing or have received a master’s or doctoral degree in a relevant field within the last five years or anticipated to be received by 10/31/2026.
• Strong background in computational modeling, data analysis, or related scientific discipline.
• Experience with statistical methods, machine learning, or AI approaches is preferred.
• Excellent written and verbal communication skills.

This opportunity is available to U.S. citizens, Lawful Permanent Residents (LPR), and foreign nationals who meet the eligibility criteria. Non‑U.S. citizen applicants should refer to program guidelines for valid immigration statuses.

• Anticipated start date:
  July 1, 2026 (flexible).
• Duration:
  One year, renewable upon recommendation and funding availability.
• Location:
  
  White Oak, Maryland.
• Full‑time position.
• Stipend:
  Monthly stipend commensurate with educational level and experience.
• Participants are not employees of FDA, DOE, or ORAU and receive no employment‑related benefits.

Zhihua Li (Zhihua.

Li.gov)