The Department of Fisheries and Wildlife at Michigan State University invites applications for the position of Postdoctoral Research Associate, under the supervision of Dr. John Robinson. The successful applicant will join an established collaborative network of researchers across five institutions (Michigan State University, the Morton Arboretum, the College of Charleston, the Missouri Botanical Garden, Mount Royal University) and contribute to an NSF-funded data integration project focused on quantifying species’ historical range shifts and population sizes using multiple data types ( https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759759). The initial appointment is for one year, with the possibility of renewal for a second year pending satisfactory performance. The successful applicant will be provided opportunities (and funding) to engage in a wide variety of professional development activities, depending on their areas of interest (e.g., international scientific meetings, mentoring in programming and scientific ethics, workshops in a targeted area of study, guest lectures). Some travel will be required of the successful applicant, including travel for annual team meetings. Opportunities to participate in planned outreach efforts associated with this project are also available.
Background: There is a pressing need to understand the factors and traits that contribute to a species’ ability to respond to rapid climate change. Although multiple data types contain information on species’ range shifts (i.e., fossil pollen data, occurrence data and ecological niche models, and population genetic data) these datasets do not always result in equivalent inferences (e.g., on the speed of range shifts). This project seeks to integrate these data types in a coherent analytical framework to infer demographic parameters (migration rates, population sizes, etc.), the location of glacial refugia, and the pace of post-glacial range movement (see Hoban et al 2019 Ecography). The statistical framework provided by Approximate Bayesian Computation (ABC) is a major component of the integrative modeling approaches we are developing. Our project team currently includes individuals with expertise in Mathematics, Statistics, Ecology, Biogeography, and Population Genetics, and we look forward to welcoming a new collaborator to the project.
Doctorate -Genetics, Ecology, Evolutionary Biology,
Applicants must have a Ph.D. in Genetics, Ecology, Evolutionary Biology, Bioinformatics, or a similar field with demonstrated experience in population genetics and a well-developed computational skillset. In particular, experience with programming (R, Python, C++), Approximate Bayesian Computation, cluster computing, and analysis of population genomic data is desirable.
Other desired qualifications include a strong work ethic, problem-solving and time management skills, experience working with a team and communicating scientific results. Applicants should demonstrate an interest in joining an established interdisciplinary research team working at the interface of statistics and ecology, and in contributing to an open-source software development project
Required Application Materials
Interested applicants should submit a cover letter, statement of research interests, and contact information for three references. In addition to the materials above, code (e.g., link to a GitHub repository) and writing samples (i.e., one or more recent publications) are also strongly encouraged, and will be considered during review.
Review of applications will begin April 23, 2020 and continue until the position is filled.
For questions about the position or application process, contact Dr. John Robinson (email@example.com).
Review of Applications Begins On
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