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

Postdoctoral Research Associate:
Genetic and Molecular Epidemiology

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.

Primary Project The position is supported by an NCI R37 (MERIT) award investigating N 6-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, and offers an unusually complete training arc for someone who wants to 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 gives the postdoc direct access to expertise in both population-level genomics and cancer epitranscriptomic biology.

A candidate trained on either side of that divide will have a genuine home in the other.

Broader Research Program This is a lab, not a single project. Beyond the primary aim, several funded and developing directions are open to the postdoc to contribute to or lead, according to their own interests and ambitions:

• 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 in 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 rather than assumed. The lab is supported by NCI K99/R00 and R37 (MERIT) awards and UVA startup funds.

Research Computing and AI Resources The lab treats large language models and AI coding agents as standard research infrastructure, on the same footing as the compute cluster or a statistical package. Fluency with these tools is part of the training you will receive here, and it is a skill the lab expects its members to carry into their independent careers. The lab covers subscriptions to frontier AI models and coding agents, including Claude, ChatGPT, and Gemini, at the highest usage tiers available, so that no one in the lab rations their own research against a token limit.

Use of these tools in the lab follows University and NIH data use requirements. Lab members also have access to the University of Virginia's high-performance computing (HPC) environment, dedicated storage, and the computational resources these workflows depend on.

Minimum Qualifications

• 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 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. Candidates from this track are actively encouraged to apply; the analytical training is part of the position, not a prerequisite for it.

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 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),…