Postdoctoral Researcher in Statistical Genetics and Computational Biology

Statistics Interest Group; Charlottesville (VA); On-site; USD 52,000 - 65,000; Full time

Job summary

The Chu Lab – Department of Genome Sciences, University of Virginia School of Medicine in Charlottesville invites applications for a Postdoctoral Researcher in Statistical Genetics and Computational Biology. The position focuses on developing deconvolution methods to integrate single-cell references with population-scale RNA-seq for cell-type-specific analyses.

We seek a quantitatively trained scientist with a strong background in statistical genetics and inference, and the ability to publish

Qualifications

• Ph.D. (or equivalent) in a quantitative field, on or before start date.
• Strong foundation in statistical inference, Bayesian methods, hierarchical models, or high‑dimensional statistics.
• Experience with statistical genetics (eQTL, GWAS, colocalization) is highly desirable.
• Proficiency in scientific programming (R and/or Python).

Responsibilities

• Develop statistical deconvolution methods to map cell-type-specific eQTLs from bulk and single-cell RNA-seq data.
• Design and implement new deconvolution frameworks and uncertainty quantification.
• Pursue independent research directions aligned with lab interests and contribute to peer-reviewed publications.

Skills

Statistical inference

Statistical genetics

R/Python programming

Publications

Single-cell & bulk RNA-seq

Education

Ph.D. in Statistics/Biostatistics/Computational Biology or related

Job description

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The Chu Lab – Department of Genome Sciences, University of Virginia School of Medicine – Postdoctoral Researcher in Statistical Genetics and Computational Biology

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

Position Title Postdoctoral Researcher in Statistical Genetics and Computational Biology

Company Information

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

The Chu Lab (www.tchulab.org) 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.

Mentorship and Career Development

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.

Duties and Responsibilities

Research Directions

• 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 BayesPrism framework toward greater accuracy, robustness, and rigorous uncertainty quantification.
• Statistical inference for cell-type-specific gene regulation. Develop principled inference methods that jointly model single-cell references, bulk expression, and genotype data — with natural extensions to linking cell-type-specific eQTLs to GWAS signals (e.g., colocalization) to dissect the genetic basis of disease.

Candidates are also encouraged to develop independent research directions aligned with the lab’s interests.

Position Qualifications

Minimum Qualifications:

Ph.D. (or equivalent) in Statistics, Biostatistics, Computational Biology, Applied Mathematics, Computer Science, Quantitative Genetics, or a related quantitative discipline, in hand by the appointment start date.

Preferred Qualifications:

• Strong foundational knowledge in statistical inference (e.g., Bayesian inference, hierarchical models, high-dimensional or nonparametric statistics).
• Background in statistical genetics - prior experience with eQTL mapping, GWAS, fine-mapping, colocalization, or related population-genetics analyses is highly desirable.
• Proficiency in scientific programming (e.g., R and/or Python)
• At least one peer-reviewed publication in the previous area of research (not necessarily biology-related).
• Genuine intellectual curiosity for solving biological problems through rigorous quantitative approaches.
• Prior experience with single-cell or bulk RNA-seq data is a plus - candidates from purely statistical or computational backgrounds are encouraged to apply.

Appointment and Application

This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding. Salary is commensurate with education and experience. This position will sponsor applicants for work visas who meet the qualifications. The start date is available immediately and is flexible. This position will remain open until filled. The University will perform background checks on all new hires prior to employment.

For questions about the position, please contact Dr. Tinyi Chu at [e-mail]. More information about the lab: www.tchulab.org.