Research Associate, Department of Public Health Sciences

The Yang Lab (PI: Yaohua Yang) at the Center for Public Health Genomics (CPHG), Department of Public Health Sciences, University of Virginia (UVA) School of Medicine is seeking a highly motivated Postdoctoral Research Associate in the field of Genetic and Molecular Epidemiology. The research program in the lab focuses on identifying genetic and molecular biomarkers for cancer risk and prognosis and investigating microbiome-host interactions in cancer development, by integrative analyses of multi-level omics data.

The successful candidate will work on ongoing projects such as DNA methylome-wide association studies (MeWAS) of cancers, N 6-methyladenosine (m6A) transcriptome-wide association studies (m6A-TWAS) of complex diseases, microbiome-host interactions in colorectal neoplasia, multi-omics analyses of lower airway microbiome in lung cancer prognosis, etc. The lab is funded by the NIH/NCI K99/R00 award (R00 CA248822) and development funds from UVA. The postdoctoral scientist will receive comprehensive and personalized training in research and career development. The lab encourages exploring new research directions and provides support to apply for intramural and/or extramural funding, e.g., K awards from NIH, to be competitive in the faculty job market.

This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding. Salary is commensurate with education and experience.

Minimum Requirements:

Ph.D., M.D., or equivalent degree in Epidemiology, Medicine, Biostatistics, Bioinformatics, or related health sciences.

Preferred Qualifications:

• Experience working with next-generation sequencing data and population-based cohort studies, as well as skills in using R and/or Python for statistical analyses and programming, are required.
  
• Excellent communication and teamwork skills.
  
• At least one peer-reviewed publication from PhD research as a lead author written in English with submitted, accepted, or published status at the time of application are preferred.
  
• Understanding the synergy of computational and experimental research and commitment to collaboration and team science.
  

For questions about this position, please contact PI at vta8we@virginia.edu .

Physical Demands:

This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings, and programs.

To Apply:

Please apply through Workday , and search for R0045124 . Internal applicants must apply through their UVA Workday profile by searching 'Find Jobs'. Complete an application online with the following documents:

• CV
  
• Cover letter
  
• C ontact 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.

This position will remain open until filled. 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.

For questions about the application process, please contact Yelena Markazyan, Academic Recruiter at ym3m@virginia.edu .

For more information about UVA and the Charlottesville community please see

http://www.virginia.edu/life/charlottesville and https://embarkcva.com/ .

The University of Virginia, i ncluding 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.