FDA Fellowship - Mechanistic Modeling and Simulation

Application

To apply, scroll to the bottom of this opportunity and click APPLY. Required documents include an application, transcripts, a current resume/CV, and a recommendation.

Description

The U.S. Food and Drug Administration (FDA) is seeking an ORISE fellow to gain experience in regulatory science research supporting pharmaceutical product quality. The project focuses on developing and applying AI, machine learning, mechanistic modeling, and simulation methods to pharmaceutical manufacturing and product characterization.

Learning Objectives

• 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.
• 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.
• Learn to evaluate AI- and model-based methodologies submitted in support of pharmaceutical development, manufacturing, and quality assessment.
• Prepare technical reports, scientific manuscripts, presentations, and other research communications.
• Work collaboratively on interdisciplinary projects with scientists, engineers, statisticians, data scientists, and regulatory reviewers.

Mentor

Jianan Zhao (jianan.zhao@fda.hhs.gov)

Research Project

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.

Potential Research Topics

• 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.
• 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.

Qualifications

• Currently pursuing or having received a master's or doctoral degree in Chemistry and Materials Sciences.

Contact Information

For questions about the nature of the research, contact the mentor at jianan.zhao@fda.hhs.gov.