Post-Doctoral Research Fellow, Biostatistics, Statistical Genetics, 'Omics

Job ID
Regular Full-Time
Fred Hutchinson Cancer Research Center
Biostatistics, Bioinformatics and Computational Biology


Cures Start Here. At Fred Hutchinson Cancer Research Center, home to three Nobel laureates, interdisciplinary teams of world-renowned scientists seek new and innovative ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases. Fred Hutch’s pioneering work in bone marrow transplantation led to the development of immunotherapy, which harnesses the power of the immune system to treat cancer. An independent, nonprofit research institute based in Seattle, Fred Hutch houses the nation’s first cancer prevention research program, as well as the clinical coordinating center of the Women’s Health Initiative and the international headquarters of the HIV Vaccine Trials Network. Careers Start Here.


A Post-Doctoral Research Fellow position is available in Drs. Charles Kooperberg and Alexander Reiner's group at the Fred Hutchinson Cancer Research Center. The successful applicant will develop and apply statistical/computational methods to analyze whole genome sequencing data and various other types of multi-omics data (DNA methylation, RNA sequencing, metabolomics, proteomics) for elucidating the genetic basis and molecular mechanisms underlying chronic conditions such as cardiovascular disease and hematologic conditions (for example clonal hematopoiesis) in multi-ethnic populations. Specific topics/application include, but not limited to trans-ethnic mapping of genetic associations, mediation and Mendelian Randomization analysis, network analysis of high-dimensional multi-omic data, and methods for somatic mutations from whole genome sequence or RNA data.


Interested candidates should submit a cover letter and CV.


The applicant must have a PhD or equivalent degree in Statistics, Biostatistics, Computer Science, Bioinformatics, Statistical Genetics, or other related fields. The candidate should have solid training in statistical methods and excellent computational skills. In addition, background in biology and genetics, and excellent organization and communication skills are a plus.

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