Virginia Tech is a public land-grant university, committed to teaching and learning, research, and outreach to the Commonwealth of Virginia, the nation, and the world. Building on its motto of Ut Prosim (that I may serve), Virginia Tech is dedicated to InclusiveVT-serving in the spirit of community, diversity, and excellence. We seek candidates who adopt and practice the Principles of Community, which are fundamental to our on-going efforts to increase access and inclusion, and to create a community that nurtures learning and growth for all of its members. Virginia Tech actively seeks a broad spectrum of candidates to join our community in preparing leaders for the world.

Position Summary:
A postdoctoral position is available in the newly instituted Uyeda lab at Virginia Tech. This lab develops and applies phylogenetic comparative methods in a variety of empirical systems with the goal of connecting analysis of macroevolutionary patterns with a process-based understanding of evolution. Candidates will have substantial opportunity to develop their own research projects and interests so long as they have overlap with the goals of the lab. Examples of current research topics include the evolution of physiological and metabolic traits, integrating biomechanical performance landscapes into comparative models, and using phenotypic ontologies in trait evolutionary models. Experience with programming (particularly in R), computational biology, Bayesian statistical modeling, quantitative genetics, and/or phylogenetics are also desirable. Candidates from either math/theory/computational or empirical backgrounds are encouraged to apply. Review of applications will begin on September 15th, 2017; but position will remain open until filled. For questions and informal inquiries, please contact Dr. Josef Uyeda at juyeda@vt.edu.

Required Qualifications:
PhD in relevant field (evolution, organismal biology, statistics, computational biology, etc.)

Preferred Qualifications:
Experience in the development of phylogenetic comparative methods, phylogenetics or a related field
Programming experience
Experience with Bayesian statistical modeling



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