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Computational Science, Engineering, Complex Systems, or Applied Mathematics PhD Student

Position ID: GRA#007

A Graduate Research Assistantship position is available at the University of Vermont as part of Vermont as part of an NSF-funded research project on Lake Champlain Basin Resilience to Extreme Events. Vermont EPSCoR’s latest Track 1 award is recruiting for this position within the next year to work on a cutting-edge, large-scale, interdisciplinary, NSF-funded study that will determine how the Lake Champlain Basin’s landscape, watershed and lake conditions respond to extreme weather events and will test policy scenarios for enhancing resilience using our comprehensive Integrated Assessment Model.  As a member of the research team, you will participate in unique learning and professional development experiences and have access to significant resources including: Alan Alda Communicating Science workshops; undergraduate mentoring and training; and over 30 research colleagues spanning numerous disciplines including hydrology, limnology, geology, engineering, environmental resources, economics, public policy and community development. 

Qualifications:We seek students interested in pursuing PhD level research in the areas of computational science, engineering, complex systems, applied mathematics or related fields. A B.A., B.S. or M.S. in engineering, computer science, applied mathematics or related field with research interests in Scientific Computing, Multi-objective Optimization, Numerical solutions of Stochastic PDEs, Machine Learning, Uncertainty Quantification, applied to inter-disciplinary research that employs social and natural science and engineering. 

This position comes with a research assistantship that is renewable through May 2021. The position will begin on January 15, 2019, or as soon as possible thereafter, and applications will be considered until the position is filled.

LabBurlington Vermont
Starts on15/01/2019
Duration/PeriodJanuary 2019-May 2021
Deadline14/12/2018 23:00
KeywordsSocial Ecological Systems Modeling; Complex Systems;

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