Postdoctoral Researcher in Statistical Genetics and Computational Biology

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The Chu Lab – Department of Genome Sciences, University of Virginia School of Medicine

The Chu Lab ((Use the "Apply for this Job" box below).) in the Department of Genome Sciences at the University of Virginia (UVA) School of Medicine is seeking a Postdoctoral Researcher to develop the next generation of statistical deconvolution methods and to use them for cell-type-specific genetic analysis. The central goal of the position is to integrate single-cell RNA-seq references with population-scale bulk RNA-seq to map cell-type-specific eQTLs, and to build the new deconvolution and statistical-inference methodology that makes this possible.

This is an ideal position for a quantitatively trained scientist with a strong background in statistical inference and statistical genetics (e.g., eQTL or GWAS analysis) who is excited to build rigorous, widely used methods at the interface of statistics and genomics.

The Chu Lab is built on the philosophy of "Mentorship as Collaboration," where trainees are valued as scientific collaborators rather than assistants. As a postdoctoral scientist in a newly established lab, you will receive individualized mentorship tailored to your career goals, defined by genuine intellectual exchange, direct technical engagement in algorithm and model development, and shared co-ownership of the science.

• Active Collaboration. The PI maintains an open-door policy, meets regularly with trainees, and is deeply involved in supporting their algorithm and model development.
• Scientific Independence. You will be supported to develop and lead your own research ideas with the freedom and computational resources required to pursue them.
• Grant Writing and Career Transition. Leveraging the PI's recent successful K99/R00 transition, you will receive step-by-step training in scientific writing, proposal preparation, and fellowship applications. Postdocs are supported and encouraged to apply for independent fellowships.
• Visibility. Full support for presenting at top-tier venues spanning statistical genetics, machine learning, and computational biology, and active assistance in building your professional network across academia and industry.

The Chu Lab is part of a vibrant interdisciplinary research community at UVA, with active collaborations across the UVA School of Medicine. The lab has full access to UVA's high-performance computing resources and core facilities supporting genomics and single-cell sequencing.

Charlottesville, Virginia is a highly livable university town nestled at the foothills of the Blue Ridge Mountains, known for its excellent quality of life, affordability relative to other U.S. research hubs, and rich cultural and outdoor offerings.

• Cell-type-specific eQTL mapping. Develop and apply statistical models that combine deconvolution with genetic association analysis to map cell-type-specific expression quantitative trait loci (eQTLs) from large bulk RNA-seq cohorts, using single-cell RNA-seq as the reference — bringing single-cell resolution to population-scale genetics without the cost of single-cell profiling every individual.
• Next-generation deconvolution algorithms. Design new statistical frameworks that integrate single-cell RNA-seq references with bulk RNA-seq to infer cell-type composition and cell-type-specific gene expression, building on and extending the lab's Bayes Prism framework toward greater accuracy, robustness, and rigorous…