FDA Fellowship - Mechanistic Modeling and Simulation

Organization

U.S. Food and Drug Administration (FDA)

FDA-CDER-

9/25/2026 3:00:00 PM Eastern Time Zone

• Applications will be reviewed on a rolling-basis.

A research opportunity is available immediately with the Food and Drug Administration (FDA),Center for Drug Evaluation and Research (CDER), Office of Pharmaceutical Quality (FDA/CDER/OPQ), located in White Oak, Maryland.

The Center for Drug Evaluation and Research (CDER) performs an essential public health task by making sure that safe and effective drugs are available to improve the health of people in the United States. As part of the U.S. Food and Drug Administration (FDA), CDER regulates over-the-counter and prescription drugs, including biological therapeutics and generic drugs. This effort covers more than just medicines.

The U.S. Food and Drug Administration’s Center for Drug Evaluation and Research, Office of Pharmaceutical Quality (FDA/CDER/OPQ), is seeking an ORISE fellow to gain experience in regulatory science research supporting pharmaceutical product quality. The project will focus on the development and application of artificial intelligence, machine learning, mechanistic modeling, mathematical modeling, and simulation methods to pharmaceutical manufacturing and product characterization.

The fellow will collaborate with and learn from multidisciplinary FDA scientists to develop data-driven and first-principles models and evaluate how they can be used to improve understanding of pharmaceutical products and manufacturing processes. The research will contribute to the development of scientific principles, evaluation strategies, and best practices for the use of computational models and AI-enabled methods in pharmaceutical quality.

• Machine learning and deep learning methods for process monitoring, anomaly detection, fault diagnosis, and prediction of critical quality attributes.
• Data-driven models for process optimization, real-time release testing, and manufacturing process control.
• Mechanistic and mathematical models of pharmaceutical unit operations, transport phenomena, material behavior, and product performance.
• Hybrid modeling approaches that combine mechanistic knowledge with machine learning.
• Model calibration, validation, uncertainty quantification, sensitivity analysis, and assessment of model credibility.
• Computational characterization of pharmaceutical products using physicochemical, structural, formulation, manufacturing, and performance-related data.
• Evaluation of AI- and model-based methodologies submitted in support of pharmaceutical development, manufacturing, and quality assessment.

• Learn to develop and apply AI and machine learning methods to pharmaceutical manufacturing and product quality challenges.
• Gain experience constructing and evaluating mechanistic, mathematical, statistical, and hybrid models of pharmaceutical systems.
• Learn to analyze complex manufacturing, formulation, material, and product-performance data to support scientific and regulatory objectives.
• Develop skills in model verification, validation, uncertainty quantification, and credibility assessment methods.
• Build knowledge of regulatory science principles and FDA frameworks related to pharmaceutical quality and model-informed approaches.
• Learn to translate computational and modeling results into scientifically defensible conclusions that support regulatory assessment activities.
• Gain experience preparing technical reports, scientific manuscripts, presentations, and other research communications.
• Develop collaborative skills by working with scientists, engineers, statisticians, data scientists, and regulatory reviewers on interdisciplinary projects.

The mentor for this opportunity is Jianan Zhao (jianan.zhao.gov). If you have questions about the nature of the research, please contact the mentor.

September 2026. 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…