Translational Pharmacology, Omics, & AI - Assistant/Associate Professor - Tenure Track

About the role

The Division of Translational and Clinical Pharmacology (TCP) at Cincinnati Children's Hospital Medical Center (CCHMC) invites applications for a Tenure-track research faculty position at the Assistant or Associate Professor level. We seek an innovative and collaborative scientist with expertise in quantitative pharmacology or toxicology, with complementary expertise in exposomics, proteomics, metabolomics, artificial intelligence/machine learning (AI/ML), quantitative modeling, systems biology, computational sciences, or related disciplines.

Responsibilities

• Develop and lead an independent, externally funded research program.
• Conduct innovative research in one or more of the following areas: pediatric pharmacology and developmental pharmacology; toxicology, exposomics, and environmental health sciences; multi-omics integration, biomarker discovery, and precision medicine; AI/ML-enabled biomedical research; PBPK, PopPK, PK/PD, QSP, systems toxicology, and mechanistic modeling; translation of NAM-derived data to human and pediatric populations; hybrid mechanistic-AI approaches for predicting exposure, disease progression, and therapeutic outcomes; develop frameworks that connect experimental models, omics data, and clinical outcomes; establish collaborations that accelerate translational applications and therapeutic innovation.
• Build collaborations within TCP and across Cincinnati Children's and the University of Cincinnati; work closely with clinicians, quantitative scientists, computational biologists, and AI researchers; mentor graduate students, postdoctoral fellows, trainees, and junior faculty; participate in institutional, national, and international scientific activities; contribute to strategic initiatives that strengthen TCP's leadership in translational pharmacology, toxicology, exposomics, and precision medicine.

Requirements

• PhD, PharmD/PhD, MD/PhD, or equivalent doctoral degree in a relevant discipline.
• Demonstrated expertise in quantitative pharmacology or toxicology, with complementary expertise in exposomics, proteomics, metabolomics, artificial intelligence/machine learning (AI/ML), quantitative modeling, systems biology, computational sciences, or related disciplines.
• Strong publication record and potential to secure extramural funding.
• Experience working with biological, clinical, environmental, pharmacological, or multi-omics datasets.
• Excellent communication skills and commitment to collaborative science.

Qualifications

• Expertise in AI/ML, PBPK, PopPK, QSP, PK/PD, systems toxicology, or mechanistic modeling.
• Experience with organoids, organ-on-chip systems, microphysiological systems, or other NAMs.
• Interest in pediatric health, developmental biology, environmental health sciences, rare diseases, or precision medicine.
• Experience integrating omics, imaging, or clinical data into predictive models.
• Familiarity with advanced computational methods, including cloud computing, generative AI, foundation models, or graph-based approaches.
• Demonstrated commitment to mentorship and collaborative team science.

Schedule

Full‑time position (40 hours per week).