Post Doctoral Scholar - Biomedical Informatics

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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.

Research activities may include common- and rare-variant association studies, survival and longitudinal analysis, multi-state disease modeling, polygenic risk prediction, gene-environment interaction, cross-population analysis, and integration of genomic data with longitudinal electronic health records. The scholar will have opportunities to lead projects using data from the Global Biobank Meta-Analysis Initiative, a collaboration involving more than 20 biobanks and more than 2.5 million participants, as well as other large-scale biobank resources. Depending on experience and project readiness, the scholar may serve as the primary analyst and lead author on cross-biobank studies.

The position provides opportunities to collaborate with investigators across the Global Biobank Meta-Analysis Initiative, The Ohio State University, and other national and international institutions. The scholar will receive mentorship in project development, manuscript preparation, scientific presentations, grant writing, and the development of an independent research program.

Minimum Education Required

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

Required 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; and commitment to rigorous, reproducible research.

Preferred Qualifications

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. Experience with high-performance computing, reproducible workflow development, scientific manuscript preparation, or collaborative consortium research is also desirable.

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