Postdoctoral Research Associate: Genetic and Molecular Epidemiology, Department of Genome Sciences

UVA Health • Charlottesville, VA

9d • $63,480 - $77,076 • Onsite

About The Position

The Yang Lab (PI: Yaohua Yang, PhD) in the Department of Genome Sciences at the University of Virginia School of Medicine is recruiting a Postdoctoral Research Associate in genetic and molecular epidemiology. The lab identifies genetic and molecular determinants of cancer risk and prognosis and investigates how interactions between the commensal microbiome and the host shape cancer development, through integrative analysis of multi-level omics data. The primary project is supported by an NCI R37 (MERIT) award investigating N6-methyladenosine (m6A) RNA modification in lung cancer, integrating epitranscriptomic profiling of human lung tissues with population-scale genetic and multi-omics data and functional validation. The project spans discovery, mechanism, and translation, offering comprehensive training in population genomics and cancer biology. The postdoc will be co-mentored by Dr. Kexin Xu and have training opportunities with Dr. Jianjun Chen, a leading m6A biologist. The lab also supports postdocs in developing independent research directions and applying for funding. Research computing and AI resources, including large language models and AI coding agents, are treated as standard research infrastructure and training is provided. Members have access to UVA's high-performance computing (HPC) environment.

Requirements

• PhD, MD, or equivalent degree in epidemiology, genetics, genomics, biostatistics, bioinformatics, molecular biology, cell biology, or a related field, awarded or expected before the start date.
• Research training in one of the following two tracks:
• Quantitative track: experience analyzing next-generation sequencing data and/or population-based cohort data, with solid programming skills in R and/or Python.
• Laboratory track: hands-on experience with DNA/RNA/protein extraction, sequencing or mass spectrometry sample preparation and library construction, and CRISPR-based genome editing.
• Clear motivation to be trained in genetic epidemiology, bioinformatics, and computational biology (for laboratory track candidates).
• Strong written and oral communication skills.
• Ability to work effectively in a collaborative, interdisciplinary team.

Nice To Haves

• At least one first-author peer-reviewed publication from doctoral research (published, accepted, or under review).
• Experience with statistical genetics methods such as genome-wide, transcriptome-wide, and proteome-wide association studies (GWAS, TWAS, PWAS), quantitative trait loci (QTL) mapping, Mendelian randomization, colocalization, or fine-mapping.
• Experience with genomic, epitranscriptomic, and/or epigenomic data, such as whole-genome sequencing (WGS), genotyping, RNA-seq, m6A-seq/MeRIP-seq, DNA methylation arrays, ATAC-seq, or single-cell assays.
• Prior experience with microbiome data analysis.
• Familiarity with machine learning or deep learning approaches applied to biological data.
• Demonstrated fluency with AI assistants and coding agents in a research setting, for example building or debugging analysis pipelines with them, or using them systematically to explore and benchmark analytical methods.

Responsibilities

• Integrate epitranscriptomic profiling of human lung tissues with population-scale genetic and multi-omics data and functional validation for lung cancer research.
• Contribute to or lead research directions in integrating genetic with bulk and single-cell multi-omics data to identify biomarkers for complex diseases.
• Investigate the impact of the commensal microbiome on the host epigenome and transcriptome.
• Conduct multi-omics analysis of the lower airway microbiome in lung cancer prognosis.
• Utilize proteogenomic approaches to identify causal proteins and repurposable drugs for chronic lung disease.
• Develop independent research directions and apply for intramural and extramural funding.
• Receive comprehensive, individualized training in genetic and molecular epidemiology, statistical genetics, bioinformatics, and computational biology, complemented by co-mentorship in cancer molecular biology.
• Utilize large language models and AI coding agents as standard research infrastructure for analysis pipeline development and benchmarking.

Benefits

• NIH NRSA stipend scale salary ($63,480 to $77,076 depending on years of prior postdoctoral experience).
• Exempt-level, benefited position.
• Access to UVA benefits.
• Subscriptions to frontier AI models and coding agents (Claude, ChatGPT, Gemini) at the highest usage tiers.
• Access to University of Virginia's high-performance computing (HPC) environment, dedicated storage, and computational resources.

What This Job Offers

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