Benaroya Research Institute at Virginia Mason (BRI) is an internationally recognized non-profit medical research institute affiliated with the Virginia Mason Health System. BRI works to advance the science that will predict, prevent, reverse and cure diseases of the immune system, to achieve its vision of a healthy immune system for every individual. Our scientific approach is to discover, translate, integrate and intervene – accelerating translation of today’s lab discoveries into tomorrow’s clinical interventions. The institute’s core values of persistent inquiry; innovation and agility; collaboration; and integrity and respect are foundational to our work.
A leader of collaborative initiatives such as the Immune Tolerance Network and Type 1 Diabetes TrialNet, BRI frequently partners with global research institutes, pharmaceutical and biotech companies to translate discoveries into clinical therapies.
Benaroya Research Institute is seeking a bioinformatics and machine learning expert who has demonstrated experience working with large multi-omics, next-generation sequencing (bulk/single-cell) and single-cell cytometery datasets. Candidate should have working knowledge and skills in statistical analysis, regression analysis, graph theory, bayesian learning and other exploratory/inferential analytical tools. Individuals with background training in Dynamical Systems Analysis and/or Deep Learning are strongly encouraged to apply.
The individual will join as a Postdoctoral Research Associate in the Systems Immunology Group which is broadly engaged in basic and applied studies using complex systems theory, high throughput techniques as well as mathematical and computational tools to understand the functioning of the immune system in health and disease. The group works closely with informaticians and immunologists in many groups at BRI, resulting in a rich environment for quantitative, computational, and laboratory collaborations in immune disease research.
The candidate will implement bioinformatics analysis algorithms and develop analytical pipelines for extracting biomarkers from large time series datasets that will provide insights into mechanisms underlying successful response to treatment in clinical trials.
This position is remote, and is open to candidates anywhere within the U.S.A.
• Analyze multi-omics data to derive relevant insights using state-of-the-art statistical methods
• Implement pipelines and algorithms for different types of biological data analysis and visualization
• Perform integrative, pathway, and network analyses to understand disease mechanisms and discover insights
• Apply machine learning models for biomarker identification and patient stratification
• Effectively communicate analysis results via presentations
• Ph.D. (with 0-3 years of relevant experience) in Bioinformatics, Computational Biology, Machine Learning or related technical discipline
• Fluency with standard tools and data formats related to gene expression, RNA-seq, enrichment analysis, genetic, genomic, or cytometry data and perform high dimensional data mining, integration, and extraction
• Experience developing, training, and evaluating machine learning models for analysis of time series datasets; working know-how of dynamical systems analysis or deep learning methods is strongly preferred
• Fluency in Python and R or Matlab programming/scripting languages
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.