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Post-Doctoral Fellow in Biostatistics
Company Information
The Ma Lab in the Division of Public Health Sciences at Fred Hutchinson Cancer Center is seeking a highly motivated post-doctoral fellow to develop novel statistical/computational methods to solve scientific problems using modern genomic data such as microbiome and single-cell sequencing data. Specific topics include, but are not limited to, high-dimensional data analysis, graphical models and network analysis, etc. This is a unique opportunity for someone who is interested in a collaborative environment and interacting with investigators with diverse experiences. We are looking for a highly motivated individual with strong background in statistical methodology and genuine interest in science. The position is available NOW for two years.
Fred Hutch is an independent, nonprofit, unified adult cancer care and research center that is clinically integrated with UW Medicine, a world leader in clinical care, research and learning. The first National Cancer Institute-designated cancer center in the Pacific Northwest, Fred Hutch’s global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy, and COVID-19 vaccines has confirmed our reputation as one of the world’s leading cancer, infectious disease and biomedical research centers. Based in Seattle, Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy, and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover new cures to the world’s deadliest diseases and make life beyond cancer a reality.
The successful candidate will work with Dr. Jing Ma in developing novel statistical/computational methods in genomics. He/she will have opportunities to join collaborative research projects within Fred Hutch and University of Washington as well as external collaborations.
The successful candidate will have
- Completed (or nearly completed) a PhD in Statistics, Biostatistics, or a closely related area prior to their appointment;
- Demonstrated a capacity to produce high quality research;
- Strong programming skills in a statistical language (e.g. R, Python).
In addition, the ideal candidate will also have
- Experience in high-dimensional data analysis.