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Employer: Fred Hutchinson Cancer Research Center

Expires: 07/01/2021

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.At Fred Hutch, we believe that the innovation, collaboration, and rigor that result from diversity and inclusion are critical to our mission of eliminating cancer and related diseases. We seek employees who bring different and innovative ways of seeing the world and solving problems. Fred Hutch is in pursuit of becoming an antiracist organization. We are committed to ensuring that all candidates hired share our commitment to diversity, antiracism, and inclusion.  The position will come with a competitive postdoc-level salary with great benefits for two years, with the ability to extend if things are going well. 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 overlooking the lake. Powerful computing resources and helpful IT staff await. 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: phylogenetic (evolutionary tree) inference is important for genomic epidemiology and for our understanding of evolution. Trees, along with associated information, are complicated objects of inference, with intertwined discrete (tree structure) and continuous (dates, rates) structure. Random-walk Markov Chain Monte Carlo, implemented in packages such as BEAST (~20,000 citations) and MrBayes (>70,000 citations), is currently the only widely-applied inference technique. We have recently developed a rich means of parameterizing tree distributions with a fixed parameter set. This renders them accessible to more modern inference techniques, such as variational Bayes. We have developed a proof-of-concept application of phylogenetic variational Bayes using modern general-purpose gradient estimators. Our collaborative group also has preliminary integrations with both PyTorch and TensorFlow.To achieve the promise of variational Bayes phylogenetics, we will develop:structure learning methods that will infer the discrete aspect of our variational approximationfitting methods that leverage the special structure of our variational phylogenetic modelsa modeling framework that integrates with PyTorch, enabling rich models that leverage covariates such as travel history. This will be a collaborative project withMarc Suchard, a leading statistician especially known for his work in “phylodynamics:” the intersection of phylogenetics, immunology, and epidemiologyMathieu Fourment, who has led the development of fixed-tree variational inference for time-tree modelsCheng Zhang (张成), who has led the development of flexible-tree variational inference.These other groups will focus more on the 3rd aspect, whereas the Fred Hutch group will focus more on the 1st and 2nd aspect.We are implementing these algorithms in our Python-interface C++17 library.QualificationsThis position requires a PhD in statistics, computer science, biology, or another relevant field. Essential skillsWe are looking for someone who has:Experience doing methods development for a challenging Bayesian estimation problemClear ability to perform independent researchThe ability to work and collaborate with a teamAdditional helpful skillsIdeally the candidate would have:Experience with PyTorch or TensorFlowExperience with C++, and with modern C++ idiomsExperience with a modern git-based workflowExperience with Docker and continuous integrationExperience developing in a Linux environmentApplyingIf you are interested in this position, please submit the following materials:Two representative publicationsA CV summarizing your education and work experience so farThe names and email addresses of three referencesA code sample showing work that you are proud of. This has to be nontrivial, but doesn’t have to be long. Ideally it would be publicly accessible, e.g. on GitHub, but if that’s not possible an emailed attachment is fine tooA statement describing your commitment and contributions toward greater diversity, equity, inclusion, and anti-racism in your career or that will be made through work at Fred Hutch is requested of all finalists.Our Commitment to DiversityWe are proud to be an Equal Employment Opportunity (EEO) and Vietnam Era Veterans Readjustment Assistance Act (VEVRAA) Employer. We are committed to cultivating a workplace in which diverse perspectives and experiences are welcomed and respected. We do not discriminate on the basis of race, color, religion, creed, ancestry, national origin, sex, age, disability (physical or mental), marital or veteran status, genetic information, sexual orientation, gender identity, political ideology, or membership in any other legally protected class. We are an Affirmative Action employer. We encourage individuals with diverse backgrounds to apply and desire priority referrals of protected veterans. If due to a disability you need assistance/and or a reasonable accommodation during the application or recruiting process, please send a request to our Employee Services Center at or by calling 206-667-4700.