Post Doctoral Scholar-Medical Oncology

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Job Title:

Department:

Postdoctoral Scholar
Department of Internal Medicine, Division of Medical Oncology

The Hayes Lab in the Department of Internal Medicine, Division of Medical Oncology at The Ohio State University is seeking a highly motivated Postdoctoral Scholar in computational biology, bioinformatics, and cancer genomics. The successful candidate will contribute to multidisciplinary research focused on cancer genomics, precision oncology, biomarker discovery, and translational cancer biology using cutting-edge computational, statistical, and multi-omics approaches. The Postdoctoral Scholar must be an expert communicator, highly organized, and possess strong writing, analytic, and grantsmanship skills. The successful candidate will work with clinically annotated tumor cohorts, clinical trial specimens, public cancer genomics resources, and internally generated molecular datasets across diverse cancer types.

Required Qualifications

• Ph.D. in computational biology, bioinformatics, biostatistics, computer science, biomedical informatics, genomics, systems biology, quantitative biology, or a related field.
• Demonstrated experience analyzing genomic, transcriptomic, or other high-dimensional biological datasets.
• Proficiency in R and/or Python and experience working in Unix/Linux computational environments.
• Familiarity with statistical methods for genomic data analysis, including differential expression, clustering, dimension reduction, pathway analysis, survival analysis, and multivariable modeling.
• Strong analytical, organizational, and problem-solving skills.
• Ability to work independently while contributing effectively within a multidisciplinary research team.
• Excellent written and verbal communication skills.
• Demonstrated record of peer-reviewed publications and scientific presentations.
• The ability to learn quickly, work independently, and work well in a team is essential.

Preferred Qualifications

• Research experience in cancer genomics, precision oncology, translational cancer research, biomarker discovery, or computational analysis of clinically annotated cancer datasets.
• Experience with multi-omics data integration, including DNA sequencing, bulk and single-cell RNA sequencing, spatial transcriptomics, methylation, proteomics, metabolomics, or digital pathology data.
• Experience with clinical trial datasets, electronic health record data, cancer registry data, or other real-world clinical datasets.
• Familiarity with public cancer genomics resources and databases, including The Cancer Genome Atlas, cBioPortal, Genomic Data Commons, dbGaP, GEO, and related resources.
• Experience with high-performance computing, cloud-based computing environments, workflow-management systems, containers, and/or software development practices.
• Experience with machine learning, predictive modeling, or artificial intelligence methods applied to biomedical data.

Additional Information:

Location:

Position Type:

Scheduled Hours:

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