Position Overview

The Oakes Laboratory at The Ohio State University is seeking an entry-level Postdoctoral Scholar to support data-driven research focused on hematologic malignancies, with an emphasis on Acute Myeloid Leukemia (AML). The successful candidate will apply advanced computational and statistical approaches to integrate and analyze multi-omics and single-cell datasets in order to uncover biologically and clinically meaningful disease subtypes, regulatory programs, and therapeutic vulnerabilities. This position provides strong mentorship, access to large clinical cohorts, and close collaboration with experimental and clinical teams.

Primary Responsibilities

• Lead computational analysis of large-scale multi-omics datasets, including transcriptomic, epigenomic (DNA methylation, chromatin accessibility), and clinical data.
• Develop, apply, and benchmark machine learning and statistical models for subtype discovery, classification, and outcome prediction.
• Contribute to the development of reproducible computational pipelines, workflows, and analysis frameworks.
• Interpret computational findings in close collaboration with experimental and clinical investigators.
• Prepare figures, manuscripts, abstracts, and grant materials for publication and presentation.
• Present research findings at lab meetings, institutional seminars, and national conferences.
• Mentor graduate students or junior trainees as appropriate.

Required Qualifications

• Ph.D. in Computational Biology, Genomics/Epigenomics, Cancer Biology or a related quantitative discipline.
• Strong experience analyzing high-dimensional biological data.
• Proficiency in R and/or Python for data analysis and visualization.
• Experience working with large datasets in an HPC or cloud computing environment.
• Demonstrated ability to work independently and collaboratively in a multidisciplinary research setting.
• Strong written and verbal communication skills.

Preferred Qualifications

• Experience with single-cell data analysis and multi-modal data integration.
• Familiarity with machine learning methods (e.g., classification models, dimensionality reduction, clustering).
• Experience analyzing epigenomic data
• Prior work in hematologic malignancies or cancer genomics.
• Track record of peer-reviewed publications.