Post Doctoral Research Fellow : Seattle, WA, United States
Job ID 5993Type Regular Full-TimeCompany Fred Hutchinson Cancer Research CenterLocation US-WA-SeattleResponsibilities
A postdoctoral research position in genetic epidemiology is available at the Fred Hutchinson Cancer Research Center. We are seeking a recent or prospective doctoral graduate (Ph.D. or equivalent qualification) with a background in related subject areas. This could include, but is not limited to, training in epidemiology, quantitative genetics, statistical genetics, or a closely related field (please note that this is NOT a laboratory-based position). The postdoctoral fellow/research associate will work directly with Drs. Charles Kooperberg and Ulrike Peters on analyzing data from genetic association studies and whole genome sequence data in large case-control and cohort studies (such as the Women’s Health initiative or the PAGE consortium) for multiple outcomes, including cardiovascular diseases, cancer, blood traits, anthropometric traits and intermediate biomarkers, such as lipids, glucose, insulin or inflammatory markers.
The postdoctoral candidate will primarily work on association studies for common and rare variants (individual variant and aggregated tests, including functional annotation) and fine mapping projects in multi-ethnic populations and in silico follow-up of genetic loci significantly associated with complex diseases. The successful applicant will work on multidisciplinary research teams to perform data analyses, develop and/or implement novel statistical methodology relevant for aggregate analyses that incorporate functional genetic information, and to publish research results. The incumbent will be expected to work independently to complete data analysis. In addition, s/he will participate in the presentation of research, as well as in the writing of publications to disseminate the analyses.
Qualifications
A Ph.D. or equivalent degree is required. The position requires a highly motivated individual with excellent written and verbal communication skills. Expertise in computational methods for the analysis of complex, high-dimensional genotype data is desirable, as is a thorough knowledge of high dimensional data resources (such as ENCODE and ROADMAP) for functional annotation.
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