Post Doctoral Scholar-Medical Oncology

About the role

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.

Responsibilities

• 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.
• Work with clinically annotated tumor cohorts, clinical trial specimens, public cancer genomics resources, and internally generated molecular datasets across diverse cancer types.

Requirements

• 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.
• Able 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.