Postdoc: Vascular Genomics & Endothelial Genetics

UNIVERSITY OF ARIZONA; Tucson (AZ); On-site; USD 60,000 - 80,000; Full time

Benefits offered by this job

Full Benefits

Job summary

The University of Arizona invites applications for a Postdoctoral Research Associate in vascular genomics and systems genetics. Join a collaborative lab exploring how genetic variation shapes endothelial cell biology and CAD risk.

You'll gain hands-on training in experimental and computational methods, including single-cell genomics, chromatin profiling, QTL mapping, and biobank data integration, with NIH-funded projects and potential for independent directions.

Qualifications

• PhD conferred prior to start date.
• At least one first-author publication in genetics/genomics or vascular/cell biology.
• Experience with next-generation sequencing data analysis (RNA-seq or scRNA-seq).
• Proficiency in scripting/programming (R, Python, or Bash).
• Experience with primary mammalian cell culture in a BSL-2 setting.

Responsibilities

• Design and execute experiments using primary human endothelial cells.
• Prepare and QC sequencing libraries (bulk RNA-seq, small RNA-seq, ATAC-seq, scRNA-seq).
• Perform genome-scale mapping and genetic colocalization with GWAS data.
• Analyze -seq datasets with standard and custom pipelines.
• Contribute to grant writing, manuscript preparation, and national presentations.
• Mentor and support trainees and lab members.

Skills

Endothelial biology

Genomics

Bioinformatics

RNA-seq pipelines

Scripting (R/Python)

Education

PhD in genetics/genomics/related field

First-author publication

Tools

RNA-seq pipelines

Single-cell sequencing

Job description

The University of Arizona invites applications for a Postdoctoral Research Associate in vascular genomics and systems genetics. Join a collaborative lab exploring how genetic variation shapes endothelial cell biology and CAD risk.

You'll gain hands-on training in experimental and computational methods, including single-cell genomics, chromatin profiling, QTL mapping, and biobank data integration, with NIH-funded projects and potential for independent directions.