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Postdoctoral Associate

Employer
Virginia Tech Biological Sciences
Location
Blacksburg, Virginia
Salary
Commensurate with experience
Closing date
Dec 30, 2019

The Draghi lab (http://draghi.biol.vt.edu/) in the Dept. of Biological Sciences at Virginia Tech is hiring a post-doctoral research associate to understand, simulate, and predict gene interactions in a bacterial model system for the evolution of metabolism. The post-doc will work directly with the PI to lead the development and application of simulations and the process of Bayesian inference based on data from grant collaborators. The successful applicant will also help design and analyze new experiments based on their results, including efforts to leverage the parametrized computational model to predict the outcomes of evolution experiments taking place in collaborators’ labs. While working as part of a collaborative, NSF-funded project spanning four lab groups, the post-doc will have ample opportunities to create new methods for analyzing data from metabolomics and deep-sequencing assays, as well as to contribute to the directions and writing of further grant applications with this group. In addition to executing a portion of the funded proposal (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714550), the post-doc will also be encouraged to develop their own side-projects and help mentor students in the lab. The initial period of funding is for one year. The PI will work closely with the successful applicant to support their applications for continued funding and to support their career development. Start date is negotiable. Please include a CV, a cover letter describing your relevant experience and goals for learning new techniques and skills, and a list of three references with contact information. Questions should be directed to Dr. Jeremy Draghi at jdraghi@vt.edu.

Required Qualifications

- A Ph.D. in Biology, Evolutionary Biology, Chemistry, Statistics, or a related field

- At lease one publication indicating competence with programming in a scientific context.

Preferred Qualifications

- Strong writing skills.
- Experience with mathematical models of metabolism and/or physiology.
- Experience with the R programming language.
- Experience with Bayesian statistics, particularly Approximate Bayesian Computation.
- Experience with mentoring students

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