Postdoctoral Research Associate in Statistical Genetics

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 expression quantitative trait loci (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. The successful candidate will lead this project, prepare manuscripts, present at scientific meetings, and release documented software.

Key qualifications:

• Ph.D. in Statistics, Biostatistics, or related quantitative field completed by start date
• Demonstrated experience developing statistical methodology with first-author methods paper including 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 desirable