Postdoc: Bayesian Genomics for Cell-type eQTL Mapping

DGS Office of External Affairs; Charlottesville (VA); On-site; USD 55,000 - 70,000; Full time

Job summary

The Chu Lab in the Department of Genome Sciences at the University of Virginia School of Medicine seeks a postdoctoral associate to develop statistical methods for mapping cell-type-specific eQTLs from bulk RNA-seq cohorts using single-cell data as reference.

The role involves extending the lab's Bayesian deconvolution framework and developing deep generative models, with opportunities for independent research within the lab's interests.

Qualifications

• The candidate must have a Ph.D. in statistics, biostatistics, or a related quantitative field by start date.
• A track record of developing statistical methodology with a first-author methods publication including 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 analyses is desirable.

Responsibilities

• Develop statistical methods to map cell-type-specific eQTLs from bulk RNA-seq with single-cell references.
• Extend the lab's Bayesian deconvolution framework and build deep generative models.
• Lead the project, prepare manuscripts, and present findings at meetings.
• Release well-documented software for community use.

Skills

Bayesian methods

Statistical inference

First-author methods paper

R or Python

Education

Ph.D. in Statistics/Biostatistics or related field

Tools

R

Python

Job description

The Chu Lab in the Department of Genome Sciences at the University of Virginia School of Medicine seeks a postdoctoral associate to develop statistical methods for mapping cell-type-specific eQTLs from bulk RNA-seq cohorts using single-cell data as reference.

The role involves extending the lab's Bayesian deconvolution framework and developing deep generative models, with opportunities for independent research within the lab's interests.