Post Doctoral Associate

University of Pittsburgh • Pittsburgh, PA

2d

About The Position

Applications are invited for a postdoctoral researcher position in the laboratory of Dr. Junmei Wang in the Department of Pharmaceutical Sciences at the University of Pittsburgh. Dr. Wang leads an interdisciplinary research program at the University of Pittsburgh School of Pharmacy focused on developing next-generation computational methods to advance rational drug discovery in the era of artificial intelligence. The laboratory develops and applies machine learning and AI-driven approaches to address challenging problems in pharmacometrics, ADME/PK/PD modeling and simulation, and computational systems pharmacology, with the goal of supporting translational and quantitative drug development. The research environment is highly collaborative and provides trainees with unique opportunities to work at the intersection of AI-enabled drug discovery, ADME- and PK/PD-guided drug development, and precision medicine.

The postdoctoral associate will work at the intersection of clinical data science, machine learning, and quantitative pharmacology. The primary focus includes PBPK modeling, population PK/PD modeling, and the development of machine learning-based approaches for pharmacokinetic modeling. The role emphasizes applying AI and deep learning methods to improve drug metabolism prediction, support virtual screening workflows, and enable data-driven drug discovery using large-scale clinical and biochemical datasets.

Requirements

• PhD in pharmaceutical sciences, pharmacometrics, computational biology, biomedical informatics, computer science, or a closely related quantitative discipline
• Strong background in PBPK modeling, population PK, or PK/PD modeling; hands-on experience with NONMEM, Simcyp, Monolix, or MATLAB is highly desirable
• Experience developing machine learning models for the prediction of drug metabolism-related properties
• Strong programming skills in Python and/or R; experience with Linux environments
• Strong interdisciplinary communication and project management skills, with ability to work independently and collaboratively in a multidisciplinary environment.

Responsibilities

• Design, implement, and validate deep learning models (e.g., GNNs, LSTMs, and multi-task learning frameworks) for predicting drug metabolism-related properties
• Develop, evaluate, and scale PBPK and population PK/PD models to optimize dosing strategies across special populations, including pediatric and clinical subgroups
• Curate, clean, and preprocess large-scale heterogeneous biomedical datasets
• Collaborate with experimental scientists, clinical researchers, and external stakeholders; analyze and interpret results; publish in peer-reviewed journals and present at national/international conferences

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree