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 and largest 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.
There is an opening for a post-doctoral fellow to work on multi-modal single cell analysis and software infrastructure in the lab of Dr. Greg Finak at the Fred Hutchinson Cancer Research Center in Seattle, WA.
About the Lab
We are part of the RGLab, the Biostatistics, Bioinformatics, and Epidemiology program and the Vaccine and Infectious Disease Division of Fred Hutch in Seattle. We develop statistical methods and software tools for the analysis of high throughput biological data with an emphasis on immunology and vaccine research. We work with bench scientists and clinicians to understand and ultimately help develop vaccines and/or cure severe diseases such HIV, malaria, and cancer. We are a diverse group, with training in Statistics, Computer Science, Web Development, Bioengineering, Bioinformatics, and Computational Biology. Learn more about us at rglab.org and see some of our work on GitHub (github.com/RGLab).
Dr. Finak is seeking a post-doctoral fellow to develop multi-modal (Ab-Seq, CITE-Seq, etc) single-cell assay data infrastructure and analysis as part a CZI Seed Network award to the Bioconductor consortium. The successful candidate will work with a motivated team to develop & implement single-cell specific data structures for storing, manipulating and interacting with multi-modal assay data such as Ab-Seq and CITE-seq, which enable simultaneous measurement of protein and RNA from the same single cell. The candidate will also work to adapt single-cell analytics methods developed in the lab (MAST https://doi.org/10.1186/s13059-015-0844-5 , FAUST
https://doi.org/10.1101/702118) to work with these data types. The candidate is expected to adhere to good software development practices (e.g. design, unit tests, documentation, code review), and participate in regular team meetings.
To be considered, candidates must have:
- PhD in Bioinformatics, Computer Science, Computational biology or equivalent field.
- R (advanced) and C++ (intermediate).
- Unix-alike operating system environment (intermediate)
- R package development (intermediate)
- Bioconductor stack (advanced)
- Software version control (e.g. git working knowledge or greater)
- Candidate should have a strong work ethic and analytical skills, an ability to work in a team, to meet deadlines and to deliver a robust, well-tested and well-documented, quality codebase
- Track record of contributions to open source software projects.
- Excellent written and oral communication skills.
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