Postdoctoral Research Associate

scholarship during the training period.

The Department of Genome Sciences at the University of Virginia is seeking a highly motivated Postdoctoral Research Associate to join the Miller Lab and contribute to the Leducq COMET Network, an international collaborative effort focused on understanding the mechanisms of vascular calcification and related cardiovascular diseases.

This position is an outstanding opportunity for a computational scientist with strong training in bioinformatics, machine learning, and large-scale genomic data analysis to work at the interface of human genetics, single-cell and spatial multi-omics, cardiovascular biology, and translational medicine. The successful candidate will develop and apply advanced computational approaches to identify disease-associated genes, pathways, cell states, and regulatory mechanisms involved in vascular calcification, atherosclerosis, and broader cardiovascular disease.

Founded in 1819 by Thomas Jefferson, the University of Virginia is renowned for its commitment to advancing knowledge, educating leaders, and cultivating informed citizenship. The Department of Genome Sciences addresses fundamental questions in biology, public health, and medicine by developing and applying state-of-the-art genetic, genomic, computational, and multi-omic approaches to complex human diseases. The Miller Lab focuses on unraveling cardiovascular disease mechanisms by integrating large-scale human genetics, single-cell and spatial multi-omics, functional genomics, and data science approaches.

As part of the Leducq COMET Network, the successful candidate will work in a highly collaborative international environment involving data scientists, genomicists, statisticians, vascular biologists, cardiologists, and other clinical and translational experts. The candidate will contribute to the development of scalable computational pipelines, machine learning workflows, and integrative analyses that enable mechanistic discovery across diverse genomic and multi-omic datasets.

The successful candidate will be expected to:

* Develop and apply computational methods for the analysis of large-scale genomic, epigenomic, transcriptomic, single-cell, spatial, and multi-omic datasets relevant to vascular calcification and cardiovascular disease.

* Build, benchmark, and maintain robust bioinformatics pipelines for data processing, quality control, integration, visualization, and reproducible analysis.

* Use machine learning and statistical approaches to identify disease-associated genes, pathways, regulatory programs, cell states, and molecular mechanisms.

* Integrate human genetics, functional genomics, and multi-omic datasets to prioritize candidate genes and causal pathways involved in vascular calcification and cardiovascular disease.

* Work closely with lab members and Leducq COMET Network collaborators to harmonize datasets, refine analysis strategies, and interpret findings in a biological and clinical context.

* Present progress in weekly group meetings and monthly consortium meetings.

* Draft manuscripts, contribute to grant applications, and support dissemination of findings through publications and presentations at national and international conferences.

* Contribute to the training and mentorship of junior lab members, including graduate students, undergraduate researchers, and computational trainees.

Required qualifications:

* PhD degree in bioinformatics, computational biology, genomics, genetics, biostatistics, statistics, computer science, biomedical engineering, systems biology, or a related quantitative discipline.

* Strong programming skills in R and Python.

* Experience working in Linux/Unix environments and using bash, high-performance computing systems, and reproducible computational workflows.

* Experience analyzing large-scale genomic or multi-omic datasets.

* Familiarity with workflow management systems such as Nextflow.

* Strong understanding of statistical analysis, data visualization, and reproducible research practices.

* Excellent written and oral communication skills.

* Demonstrated ability to work both independently and as part of a collaborative, cross-functional team.

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