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Job Title:
Assistant or Associate Professor of Pharmaceutical Sciences (AI Focus)
Department:
Pharmacy | Pharmaceutics and Pharmacology
The Division of Pharmaceutics and Pharmacology in the College of Pharmacy at The Ohio State University invites applications at the Assistant or Associate Professor level for a tenure-track or tenured faculty position in Pharmaceutical Sciences. With a research emphasis on contemporary approaches for discovery, development, or optimization of drug therapies and/or models of disease and drug resistance, competitive applicants will have established or developing research programs that integrate artificial intelligence and other emerging computational approaches to address fundamental and translational challenges in the pharmaceutical sciences.
Ohio State’s university-wide AI(X) Hub is a multidisciplinary initiative that brings together researchers across 15 colleges to advance artificial intelligence for the public good. AI(X) Hub is expanding Ohio State University’s research community through recruitment of 100 AI-focused faculty members and is organized around six strategic pillars, including AI for Health. Through this initiative and the broader collaborative environment at Ohio State, the successful candidate will have opportunities to work with AI researchers, other pharmaceutical scientists, basic and translational biomedical investigators, clinicians, and industry partners across Ohio State.
Research areas of interest include, but are not limited to:
Innovative integration of artificial intelligence (AI) and machine learning (AI/ML) in pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation
Quantitative systems pharmacology (QSP) and physiologically based pharmacokinetic (PBPK) modeling
Mining and analysis of published and real-world datasets for disease modeling, biomarker discovery, patient stratification, and predictive modeling of efficacy and toxicity
Development of digital twins and simulation-based models to forecast drug behavior across biological scales—from subcellular to population levels—including early ADME predictions, image-based drug response modeling, and immunomodulation of tumor microenvironments
Enhancing translational research efficiency and applying computational models to clinical trial design and disease-specific contexts
AI-driven integration of genomic data to enable highly personalized treatment strategies tailored to individual genetic and disease profiles
Collaborative research with experts…