Postdoctoral Research Associate in Statistical Genetics

The Rector & Visitors of the University of Virginia; Charlottesville (VA); On-site; USD 52,000 - 68,000; Full time

Job summary

The Chu Lab at the University of Virginia invites applications for a postdoctoral position in statistical genetics. The appointee will develop statistical methods for mapping cell-type-specific eQTLs using bulk and single-cell data, extending the BayesPrism framework and collaborating across Genome Sciences and UVA.

The successful candidate will lead model development, simulations, manuscript preparation, and code release. A PhD in a quantitative field is required; visa sponsorship is available.

Qualifications

• Ph.D. completed by the start date.
• First-author methods paper with derivation and implementation.
• Experience with hierarchical Bayesian models, mixed models, latent-variable models, or high-dimensional inference.
• Proficiency in R or Python is required; experience with eQTL or GWAS is desirable.

Responsibilities

• Develop and implement statistical models and inference procedures.
• Validate methods through simulation and held-out data.
• Apply methods to study cohort in collaboration with UVA faculty.
• Prepare manuscripts for peer-reviewed journals and present at meetings.
• Release documented software and pursue independent research directions.

Skills

Hierarchical Bayesian models

Mixed models

High-dimensional inference

R or Python

Education

Ph.D. in Statistics/Biostatistics/quantitative field

Tools

R

Python

Job description

Postdoctoral Research Associate in Statistical Genetics

The Chu Lab (www.tchulab.org) in the Department of Genome Sciences at the University of Virginia School of Medicine invites applications for a postdoctoral position in statistical genetics. The lab is well funded, and the position involves close collaboration with faculty within the Department of Genome Sciences and across the University.

The Project

The postdoctoral associate will develop statistical methods for mapping cell-type-specific expression quantitative trait loci (eQTLs) from bulk RNA-seq cohorts, using single-cell RNA-seq data as a reference. The work builds on the lab's Bayesian deconvolution framework, BayesPrism (Nature Cancer, 2022) - the journal's most cited primary research article since 2022 - and extends it to a joint statistical model of the single-cell reference, bulk expression, and genotype.

In parallel, the lab is developing deep generative models for statistical deconvolution, and the associate will have the opportunity to work on this direction as well. Candidates with a statistical background who are interested in moving into deep generative modeling are encouraged to apply; the PI provides hands‑on training in this area. The methods will be applied to large-scale bulk and single-cell transcriptomic datasets with matched genotypes, and the resulting cell‑type-specific eQTLs will be integrated with GWAS. The successful candidate will lead this project.

Responsibilities & Mentorship

Responsibilities include developing and implementing the statistical models and inference procedures, validating them through simulation and held‑out data, applying them to the study cohort in collaboration with faculty in Genome Sciences and across the University, preparing manuscripts for peer‑reviewed journals, presenting at scientific meetings, and releasing documented software. The associate will also be encouraged to develop independent research directions within the lab's interests.

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. The PI works closely with trainees on model and algorithm development, provides hands‑on training in deep generative modeling for those coming from a statistics background, and offers training in scientific writing and grant preparation; postdoctoral associates are encouraged and supported to apply for independent fellowships.

The lab has full access to UVA's high‑performance computing resources and genomics core facilities. Charlottesville is a university town at the foothills of the Blue Ridge Mountains with a high quality of life and a cost of living below that of most U.S. research centers.

Qualifications

• Ph.D. in Statistics, Biostatistics, or a related quantitative field, completed by the start date.
• Demonstrated experience developing statistical methodology, especially in statistical inference, evidenced by a first‑author methods paper (published or accepted) that includes the candidate's own derivation and implementation.
• Working knowledge of hierarchical Bayesian models, mixed models, latent‑variable models, or high‑dimensional inference.
• Proficiency in R or Python.
• Experience with eQTL or GWAS analysis is desirable (but not strictly required).

Appointment & How to Apply

This is a 12-month appointment, renewable contingent on satisfactory performance and the availability of funding. Salary is competitive and commensurate with experience, with full benefits. The start date is flexible. Visa sponsorship is available. The position will remain open until filled.

Equal Opportunity and Background Checks

The University of Virginia conducts background checks on all new hires and is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.

The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment.