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The Division of Translational and Clinical Pharmacology at Cincinnati Children’s Hospital Medical Center invites applications for a tenure-track faculty position at the Assistant or Associate Professor level. We seek a computational scientist with expertise in quantitative pharmacology, systems pharmacology, or AI/ML.
Applicants will develop independent, externally funded programs using multi-omics and clinical data to improve pediatric therapeutics, disease mechanisms, and biomarker discovery
Location S
Transl and Clin Pharmacology
Day (United States of America)
Full time
40
1
Regular
The Division of Translational and Clinical Pharmacology (TCP) at Cincinnati Children's Hospital Medical Center (CCHMC) invites applications for a Tenure-Track faculty position at the Assistant or Associate Professor level. We seek an innovative and collaborative computational scientist with expertise in quantitative pharmacology, systems pharmacology, artificial intelligence/machine learning (AI/ML) or bioinformatics. Complementary expertise in exposomics, proteomics, metabolomics, multi-omics integration, mechanistic modeling, or related quantitative disciplines is highly desirable.
The successful candidate will develop an independent, externally funded research program leveraging large-scale biological, clinical, environmental, and/or multi-omics datasets to understand how drugs, environmental exposures, nutrition, microbiome-derived factors, and genetic variation influence pediatric disease, therapeutic response, and lifelong health outcomes. Particular emphasis will be placed on developing computational frameworks that integrate proteomic, metabolomic, exposomic, genomic, and clinical data to identify disease mechanisms, discover biomarkers, predict therapeutic response, enable drug repurposing, and accelerate treatment development for pediatric and rare diseases.
Research programs that integrate exposomics, proteomics, metabolomics, human-relevant New Approach Methodologies (NAMs), patient-derived organoids, microphysiological systems, and real-world clinical data with AI/ML-enabled predictive frameworks are particularly encouraged. Applications focused on rare diseases, precision therapeutics, drug repurposing, biomarker discovery, disease progression modeling, digital twins, or model-informed precision dosing are of special interest.
TCP is internationally recognized for leadership in pediatric clinical and translational pharmacology, pharmacometrics, quantitative proteomics, metabolomics, exposomics, physiologically based pharmacokinetic (PBPK) modeling, quantitative systems pharmacology (QSP), environmental health sciences, model-informed precision dosing (MIPD), and model-informed drug development (MIDD). The division is actively expanding its computational capabilities in AI/ML, biomedical data science, systems biology, multi-omics analytics, and predictive modeling to enable the next generation of pediatric therapeutics and precision medicine.
At Cincinnati Children’s, we come to work with one goal: to make children’s health better. We believe in a holistic team approach, both in caring for patients and their families, and in advancing science and discovery. We strive to do better and find energy and inspiration in our shared purpose. If you want to be the best you can be, you can do it at Cincinnati Children’s.
We Embrace Innovation—Together. We believe in empowering our teams with the tools that help us work smarter and care better. That’s why we support the responsible use of artificial intelligence. By encouraging innovation, we’re creating space for new ideas, better outcomes, and a stronger future—for all of us.
Cincinnati Children’s is proud to be an Equal Opportunity Employer committed to creating an environment of dignity and respect for all our employees, patients, and families. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, genetic information, national origin, sexual orientation, gender identity, disability or protected veteran status. EEO/Veteran/Disability