Benaroya Research Institute at Virginia Mason has a bold mission: Predict, prevent, reverse and cure immune system diseases, from autoimmune disease to cancer to COVID-19. We examine the immune system in both health and disease to understand how disorders start and how to rebalance the immune system back to health. Equipped with innovative tools and robust biorepositories, our team chips away at the biggest mysteries behind these conditions to work toward our vision of a healthy immune system for all.
As an independent non-profit organization within Virginia Mason Franciscan Health, we collaborate with clinicians to accelerate the path from innovative lab discoveries to life-changing patient care
This position is open to candidates located anywhere within the U.S.A. The candidate will have the option to work remotely from home.
The Center for Systems Immunology at Benaroya Research Institute (BRI) seeks to recruit an outstanding candidate for a Postdoctoral Fellowship in Dynamical Systems Biology.
The goal of our group is to study and characterize the dynamical behavior of the immune system in health and disease and develop an operational understanding of how nonlinearities in interactions among the system components govern response to external perturbations such as vaccines and drugs. While our primary focus is the immune system, we explore longitudinal behavior with multiscale datasets including, but not limited to, cytometry, single- and bulk- RNA-seq, proteomics and perturbation-omics data.
The phenomena that we study include all kinds of time-varying behavior such as breadth of immunotype heterogeneity across populations, resilience of immunotype variability over time, and acute responses to vaccines and drugs. The candidate will work closely with other bioinformaticians and immunologists at BRI, resulting in a rich environment for quantitative, computational, and laboratory collaborations in immune disease research.
• Ph.D. (with 0-3 years of relevant experience) in Bioinformatics, Computational Biology, Machine Learning or related technical discipline
• Demonstrated experience in integrating and extracting patterns from high-dimensional multi-omics, next-generation sequencing (bulk/single-cell) or single-cell cytometery datasets.
• Experience developing, training, and evaluating machine learning or kinetic models for analysis of time series datasets; working know-how of dynamical systems analysis or deep learning methods for sequential data analysis is strongly preferred
• Working knowledge and skills in statistical analysis, regression analysis, graph theory, bayesian learning and other exploratory/inferential analytical tools is a must.
• Fluency in Python or R or Matlab programming/scripting languages
• Implement bioinformatics analysis algorithms and develop analytical pipelines for extracting biomarkers and patterns from large time series datasets that will provide insights into mechanisms underlying successful response to treatment in clinical trials.
• Perform integrative, pathway, and network analyses on multi-omics datasets to understand disease mechanisms and discover insights
• Effectively communicate analysis results via presentations and visualizations
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, citizenship, disability or protected veteran status.