Computational Research Associate

California, United States
25 Jun 2020
End of advertisement period
25 Aug 2020
Contract Type
Fixed Term
Full Time

Job Code: 6438
Job Grade: R99

Department/DeptId: Medicine / Stanford Cancer Institute
PI Name: Christina Curtis, PhD Assistant Professor of Medicine & Genetics

3 Years Fixed Term at 100% FTE

Project Description/Role of AS-R:

Under the direction of the Principal Investigator, the Research Engineer (Computational Research Associate) will be part of an interdisciplinary research team working on novel technological and methodological approaches to tackle outstanding problems in cancer biology with a focus on quantifying tumor evolution and the development of robust biomarkers. He/she will develop and apply state-of-the-art statistical and computational methods to interpret high-throughput (epi)genomic and proteomic measurements from clinical cancer samples and relevant models systems and will be responsible for maintaining in-house and established computational pipelines for high-throughput omic analyses. The candidate will contribute to the evaluation of the performance of novel technology platforms and assays and will manage and archive large genomic data sets. The candidate will interact with other computational scientists, as well as experimental biologists and clinicians to test, validate, and refine hypotheses. He/she will present results and progress updates and contribute to manuscript preparation and will be expected to remain up to date on advances in cancer genomics by attending seminars and reading relevant literature.


PhD in Computer Science, Computational Biology, Bioinformatics, or Statistics

Additionally, significant experience in genomic data analysis, bioinformatics tools, and high performance computing is expected and should be accompanied by a relevant publication record. Fluency in multiple programming languages (C/C++, R, Perl, Python) is essential and must be accompanied by excellent problem solving and communication skills.


A background in cancer genomics is highly advantageous as is experience in mathematical modeling, stochastic simulations, and population genetics.

Contact information:

“Stanford University is an equal opportunity employer and is committed to increasing the diversity of its faculty and academic staff. It welcomes nominations of and applications from women and members of minority groups, as well as others who would bring additional dimensions to the University’s research, teaching and clinical missions.”

Additional Information

  • Schedule: Full-time
  • Employee Status: Regular
  • Requisition ID: 76742

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