Postdoctoral Research Associate in Statistical Genetics

University of Virginia • Charlottesville, VA

7d • Onsite

About The Position

The Chu Lab at the University of Virginia School of Medicine is seeking a Postdoctoral Research Associate in Statistical Genetics. This well-funded position involves developing statistical methods for mapping cell-type-specific expression quantitative trait loci (eQTLs) from bulk RNA-seq data, utilizing single-cell RNA-seq data as a reference. The work will build upon the lab's existing Bayesian deconvolution framework, BayesPrism, and extend it to a joint statistical model. The associate will also have the opportunity to work on deep generative models for statistical deconvolution, with hands-on training provided by the PI for candidates interested in this area. The developed methods will be applied to large-scale transcriptomic datasets with matched genotypes, and the resulting eQTLs will be integrated with GWAS. The successful candidate will lead this project.

Requirements

• 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.

Nice To Haves

• Experience with eQTL or GWAS analysis is desirable (but not strictly required).

Responsibilities

• Developing and implementing statistical models and inference procedures.
• Validating models through simulation and held-out data.
• Applying models to the study cohort in collaboration with faculty.
• Preparing manuscripts for peer-reviewed journals.
• Presenting at scientific meetings.
• Releasing documented software.
• Developing independent research directions within the lab's interests.

Benefits

• Full benefits
• Competitive salary commensurate with experience
• Individualized mentorship tailored to career goals
• Hands-on training in deep generative modeling
• Training in scientific writing and grant preparation
• Support for applying for independent fellowships
• Access to UVA's high-performance computing resources
• Access to genomics core facilities
• Visa sponsorship is available

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree