Postdoctoral position to develop Bayesian phylogenetic methods for B cell receptor sequence lineages

15 Jan 2021, by Erick


The goal of our project is to develop, implement, and apply Bayesian evolutionary algorithms for the analysis of B cell receptor sequence lineages. These lineages are important for understanding the events leading to the development of high-affinity antibodies.

We are motivated to:

  • use a Bayesian approach to appropriately account for uncertainty in phylogenetic inferences (which is considerable in this case)
  • fit and use complex models of somatic hypermutation and selection that violate the commonly-applied IID assumption in phylogenetics
  • develop efficient, elegant, and robust implementations of the newly developed methodology and make them available in open-source software for the community.


We will work together to develop novel models, implement these models in open-source software, write tests to verify correctness, apply the methods to a variety of data sets, and write papers describing the results.

We’ll have the opportunity to work with a broad range of leading researchers, including:

  • biologists Jesse Bloom, Leslie Goo, Julie Overbaugh, Gabriel Victora, and their labs
  • statisticians Vladimir Minin, Noah Simon, Marc Suchard, and their groups.


The position will come with a competitive postdoc-level salary with great benefits for two years, with possibility of extension. The environment is lively yet casual, with a strong emphasis on collaborative work. The Center is housed in a lovely campus on Lake Union a short walk from downtown, and a slightly longer walk from the University of Washington. The Matsen group is in the newly-remodeled Steam Plant building (see photo) overlooking the lake. Powerful computing resources and helpful IT staff await. Ideally you’d want to be on campus (when that’s possible again) but long-term remote work is possible from these states: Alabama, Alaska, Arizona, California, Colorado, Hawaii, Idaho, Maryland, Minnesota, Montana, New York, Ohio, Oregon, South Carolina, and Texas.

We believe that science is for everyone. We have had researchers with a variety of backgrounds, including Latinx, Black, Asian, and Middle Eastern. We have had women, men, gay, and straight, and we welcome people of all sexual orientations and gender identities. We have had successful high schoolers, postdocs, people who were the first in their family to attend college, and one who had decided that college wasn’t for them. We have had researchers with backgrounds in biology, physics, statistics, math, and computer science.

We acknowledge the historical and present barriers for underrepresented groups, and work to increase diversity, equity and inclusion in computational biology. Members of underrepresented groups are especially encouraged to apply.

You can find out more about our group by visiting: 


The ideal candidate for this project would have experience with the statistical underpinnings of Bayesian phylogenetic analysis, as well as experience implementing models in code. However, we welcome applications from candidates with less statistical expertise but a deep desire to expand their skills in this area. We hope applicants will want to improve their coding abilities through clean coding practices, code review, and a modern development workflow. The ideal candidate would also be motivated to improve biological understanding through computation, and so would be enthusiastic about working closely with our leading biologist collaborators.

Minimum qualifications:

  • Ph.D. in biology, computer science, math, or another relevant area
  • Solid foundation in Bayesian phylogenetics or other challenging Bayesian estimation problem
  • Computer programming experience
  • Clear ability to perform independent research

If you are interested in this position, please submit the following materials:

  • Two representative publications
  • A code sample
  • CV

Informal inquiries are welcome:

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