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Postdoctoral associate in viral evolution and phylodynamic modeling

We seek a talented and dedicated post-doctoral researcher with a background in computational biology, phylogenetics and bioinformatics, to analyze experimental and field data for virus infections in pigs (porcine reproductive and respiratory syndrome virus - PRRSV), and to develop phylodynamic models on the effects of partial immunity on virus transmission, evolution, and multi-strain dynamics. This position is part of the international collaborative project funded by a USDA-NSF-NIH Ecology and Evolution of Infectious Diseases (EEID) grant. The position will be supervised by Dr. Kim VanderWaal, but will involve working within a large team of scientists both at the University of Minnesota and the University of Edinburgh.

This project involves both analyzing micro-evolutionary patterns using NGS data from infected pigs with different immune backgrounds, and macro-evolutionary patterns using existing large-scale sequence datasets. Phylodynamic modeling will be coupled with detailed spatial, animal movement, and immunity-related data to help quantify how cross-immunity and population connectivity influence co-circulation of strains and fitness of variants. PRRSV causes one of the most important pig diseases worldwide, so there is great interest in effective control measures.

More information on the project is available at:

https://vetmed.umn.edu/news/new-3-million-grant-helps-researchers-tackle-porcine-reproductive-and-respiratory-syndrome-virus


Candidates must have completed a Ph.D. degree by start date. Strong organizational, quantitative analysis, and writing skills are necessary. Experience with phylodynamics, molecular epidemiology, or bioinformatics is also required. Desired qualifications include excellent oral/written communication; leadership and interpersonal skills; previous experience with livestock epidemiology or production; and demonstrated ability to work in teams. Candidates with a background in epidemiology, veterinary medicine, statistics, management of large datasets will have preference.

Due Date: March 25, 2021.

Application: Apply online at: employment.umn.edu, job number: 339329


Review of applications will begin immediately and continue until the position is filled. Applications should include: (1) a cover letter describing research interests and fit with this position, and a possible start date, (2) a curriculum vitae, and (3) and names of three references. Please contact Dr. Kim VanderWaal (kvw@umn.edu) for further details.