Post Doctoral Scholar - Biomedical Informatics

About the role

The Post Doctoral Scholar will pursue advanced research training in statistical genetics, genetic epidemiology, and longitudinal biobank research. Under the mentorship of Dr. Ying Wang in the Department of Biomedical Informatics, the scholar will investigate the genetic architecture of disease onset, progression, multimorbidity, and clinical outcomes using large-scale genomic, electronic health record, and longitudinal cohort data. The position emphasizes rigorous statistical methodology, reproducible computation, and clinically meaningful research questions.

Responsibilities

• Conduct common- and rare-variant association studies
• Perform survival and longitudinal analysis
• Engage in multi-state disease modeling
• Develop polygenic risk prediction models
• Investigate gene-environment interactions
• Carry out cross-population analyses
• Integrate genomic data with longitudinal electronic health records
• Lead projects using data from the Global Biobank Meta-Analysis Initiative (>20 biobanks, >2.5 million participants) and other large-scale biobank resources
• Serve as primary analyst and lead author on cross-biobank studies, depending on experience and project readiness
• Collaborate with investigators across the Global Biobank Meta-Analysis Initiative, The Ohio State University, and other national and international institutions
• Receive mentorship in project development, manuscript preparation, scientific presentations, grant writing, and development of an independent research program

Requirements

Doctoral degree or equivalent terminal degree in biostatistics, statistical genetics, genetic epidemiology, bioinformatics, computational biology, biomedical informatics, epidemiology, or a related quantitative discipline.

Qualifications

• Demonstrated research experience in statistical genetics, genetic epidemiology, biostatistics, computational genomics, longitudinal data analysis, or a related quantitative field
• Proficiency in R and/or Python and experience working in Unix/Linux computing environments
• Experience managing and analyzing large genomic, epidemiologic, or clinical datasets
• Strong written and oral communication skills
• Ability to work independently and collaboratively
• Commitment to rigorous, reproducible research
• Preferred: Experience with genome-wide or rare-variant association studies, survival analysis, longitudinal or multi-state modeling, polygenic risk scores, gene-environment interaction, electronic health record data, or large-scale biobank resources
• Preferred: Experience with high-performance computing, reproducible workflow development, scientific manuscript preparation, or collaborative consortium research