Postdoctoral Fellow – Russell Bowler Lab
Cleveland
Cleveland Clinic Main Campus
X1004
Variable
12:00am-12:00am
Highly motivated individuals with a PhD or MD/PhD in relevant area are encouraged to apply. This training position, under the direct supervision of a Cleveland Clinic Principal Investigator will provide practical training and experience in a research setting. This position is appointed through Cleveland Clinic Research. The successful applicant will be able to demonstrate a commitment to research, a collaborative work ethic and strong communication skills, both written and verbal. This program is expected to be completed within 5 years. Compensation follows the NIH NRSA Postdoctoral Salary Scale based on total years of postdoctoral experience.
We are seeking a highly motivated Computational Biology Post-Doctoral Fellow to develop and apply advanced multi-omic network and AI/ML models using large-scale human populations, including TOPMed, UK Biobank, and other deeply phenotyped cohorts. A strong foundation in biostatistics and statistical modeling is essential for integrating genomic, transcriptomic, proteomic, metabolomic, imaging, and clinical data using both cloud-based and on-premises high-performance computing resources. The fellow will work in a dynamic, interdisciplinary environment alongside clinician-scientists, statistical geneticists, and functional genomics teams, with opportunities to experimentally validate computational discoveries. This position provides exceptional opportunities to build an independent research trajectory and to collaborate across the international Cleveland Clinic enterprise, contributing to translational programs in precision medicine, data-driven discovery, and complex disease biology.
Principal Investigator: Russell Bowler, MD, PhD
Cleveland Clinic Research Department: Genomic Sciences and Systems Biology
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All application materials should be submitted directly via email to Amy Petriella at petriea@ccf.org.
Keywords: Postdoc, Research, Multi-Omics, AI, Machine Learning, Genomics, Systems Biology, Omics, Genetics
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