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Postdoctoral Research Associate

University of Virginia
Newcomb Hall, VA
Closing date
Nov 26, 2022

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The Miller Laboratory at the University of Virginia ( seeks a computational postdoctoral research associate with a background in a quantitative field to apply systems genomic analyses to investigate inherited vascular diseases and cardiomyopathies. Available projects include the integration of whole genome sequencing, single-nucleus (e.g. snRNA-seq, snATAC-seq, snCUT&Tag), and spatial (spatial transcriptomics, MERFISH, and proteomics) datasets from healthy and diseased subjects of ancestrally diverse backgrounds. The overall goals of this work are to apply novel integrative statistical and machine learning models to better classify disease subtypes, prioritize causal gene regulatory networks and identify clinically relevant targets/biomarkers for vascular and myocardial diseases. There will also be opportunities to investigate the functional impact of regulatory variation and well-defined pathogenic mutations of rare diseases identified in our biobank and through collaborations with external partners.

The candidate will have access to a broad range of population genetic and genomic datasets through consortia projects (e.g. TOPMed, CHARGE, MESA etc), high performance computing resources, and will be exposed to a stimulating, multi-disciplinary and inclusive environment in the Center for Public Health Genomics (CPHG). This work will involve close collaborations with other CPHG labs to broaden training in both wet lab and computational biology skills. The candidate will also benefit from strong collaborations with faculty members in the Departments of Computer Science, Biomedical Engineering and Biochemistry and Molecular Genetics.


* PhD in a quantitative field (e.g. bioinformatics, statistics, engineering, physics, computer science, genetics, or related field) required in hand by appointment start date.
* Experience in either statistical or computational data analysis, network analyses, is preferred, including proficiency in R, python (or other scripting languages).
* Experience in machine learning or deep learning is preferred but not required.
* Strong working knowledge of biomedical fields such as human genetics, molecular/cell biology, physiology etc.
* Excellent written and verbal communication skills are required, as is the ability to work in a cross-functional team.
* Experience in machine learning or deep learning is preferred but not required.

As the project progresses, there will be ample opportunities to present in national and international scientific meetings. The candidate will also be strongly encouraged to apply for internal and external grant and fellowship opportunities.

The University will perform background checks on all new hires prior to employment. A completed pre-employment health screen is required for this position prior to employment.

To Apply:
Please apply through Workday (, or by searching for R0037207. Internal applicants must apply through their UVA Workday profile by searching 'Find Jobs.' Complete an application online with the following documents:

* CV
* Cover letter
* Contact information for 3 references

Upload all materials into the resume submission field, multiple documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission.
Applications that do not contain all required documents will not receive full consideration.

Physical demands
This position can spend extensive periods of time standing and squatting while conducting experiments or caring for animals. Also requires keyboarding and other bench work and animal procedures requiring finer finger manipulation.

COVID Vaccination Requirement and Guidelines
Please visit the UVA COVID-19 Job Requirements and Guidelines webpage prior to applying for current information regarding vaccination requirements and guidelines for employment at UVA.

The University of Virginia, including the UVA Health System which represents the UVA Medical Center, Schools of Medicine and Nursing, UVA Physician's Group and the Claude Moore Health Sciences Library, are fundamentally committed to the diversity of our faculty and staff. We believe diversity is excellence expressing itself through every person's perspectives and lived experiences. We are equal opportunity and affirmative action employers. All qualified applicants will receive consideration for employment without regard to age, color, disability, gender identity or expression, marital status, national or ethnic origin, political affiliation, race, religion, sex (including pregnancy), sexual orientation, veteran status, and family medical or genetic information.
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