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

    Highlights

    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). 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.

    Numbers & Facts

    Location
    Websitecincinnatichildrens.org/news/release/2024/us-news

    Description

    Primary Location

    Location S

    Department

    Transl and Clin Pharmacology

    Shift

    Day (United States of America)

    Schedule

    Full time

    Weekly Hours

    40

    FTE

    1

    Employee Status

    Regular


     

    Assistant or Associate Professor (Tenure-Track)


    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.

     

     Required Qualifications

    • PhD, MD/PhD, PharmD/PhD, or equivalent in computational biology, bioinformatics, biomedical data science, pharmacometrics, systems pharmacology, toxicology, pharmacology, engineering, computer science, or a related discipline.
    • Demonstrated expertise in computational and quantitative approaches applied to biomedical research.
    • Experience analyzing and integrating large-scale biological, environmental, clinical, proteomic, metabolomic, exposomic, genomic, or other multi-omics datasets.
    • Strong publication record and demonstrated potential for independent extramural funding.
    • Commitment to collaborative and team-based translational science.

     

    Preferred Qualifications

    • Expertise in AI/ML, deep learning, generative AI, foundation models, causal inference, graph learning, or advanced statistical methods.
    • Experience integrating exposomics, proteomics, metabolomics, genomics, imaging, wearable sensor, or clinical datasets.
    • Expertise in PBPK, PopPK, PK/PD, QSP, systems biology, or mechanistic modeling.
    • Interest in rare diseases, pediatric therapeutics, precision medicine, environmental health, or drug repurposing.
    • Experience developing computational tools or predictive models for biomarker identification, disease progression, or therapeutic optimization.
    • Experience working with cloud computing, high-performance computing, or large biomedical datasets.

    TCP Description

    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.

    About Us

    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. 

     Cincinnati Children's is: 

     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.

    Comprehensive job description provided upon request.

    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

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