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

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.

About the Role

The position is supported by an NCI R37 (MERIT) award investigating N6-methyladenosine (m6A) RNA modification in lung cancer. The project integrates epitranscriptomic profiling of human lung tissues with population-scale genetic and multi-omics data and functional validation. It spans discovery, mechanism, and translation, offering a complete training arc for work at the interface of population genomics and cancer biology.

The postdoc will be co-mentored by Dr. Kexin Xu, Professor of Genome Sciences, whose laboratory specializes in m6A biology in cancer, and will have the opportunity to receive training from Dr. Jianjun Chen, Chair of Systems Biology at the Beckman Research Institute of City of Hope, a world-leading m6A biologist. This paired mentorship provides direct access to expertise in both population-level genomics and cancer epitranscriptomic biology.

Research Opportunities

Beyond the primary project, several funded and developing directions are open to the postdoc to contribute to or lead, including:

• Integrating genetic with bulk and single-cell multi-omics data to identify biomarkers for complex diseases
• Investigating the impact of the commensal microbiome on the host epigenome and transcriptome
• Multi-omics analysis of the lower airway microbiome in lung cancer prognosis
• Proteogenomic approaches to identifying causal proteins and repurposable drugs for chronic lung disease

The lab actively supports postdocs in developing independent research directions and applying for intramural and extramural funding, including NIH K awards, with the explicit goal of building a competitive record for the faculty job market.

Training

The successful candidate will receive comprehensive, individualized training in genetic and molecular epidemiology, statistical genetics, bioinformatics, and computational biology, complemented by co-mentorship in cancer molecular biology. Training is tailored to the candidate's starting point.

The lab treats large language models and AI coding agents as standard research infrastructure. Fluency with these tools is part of the training, and the lab covers subscriptions to frontier AI models (e.g., Claude, ChatGPT, Gemini) at the highest usage tiers. Lab members also have access to UVA’s high-performance computing (HPC) environment and dedicated storage.

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 tracks is highly preferred:

• 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, together with clear motivation to be trained in genetic epidemiology, bioinformatics, and computational biology.

Preferred Qualifications

• At least one first-author peer-reviewed publication from doctoral research (published, accepted, or under review).
• Experience with statistical genetics methods such as GWAS, TWAS, PWAS, QTL mapping, Mendelian randomization, colocalization, or fine-mapping.
• Experience with genomic, epitranscriptomic, and/or epigenomic data (e.g., 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.
• Strong written and oral communication skills, and the ability to work effectively in a collaborative, interdisciplinary team.

Physical Demands

This is primarily a sedentary role involving extensive use of desktop computers. Occasional travel to attend meetings and programs is required.

Pay

Salary follows the NIH NRSA stipend scale for postdoctoral trainees, ranging from $63,480 to $77,076 for FY 2026, depending on years of prior postdoctoral experience.

Schedule

This is a restricted, benefited position with an initial appointment of one year, renewable annually based on performance and funding availability. The position is based in Charlottesville, VA, and must be performed fully on-site.