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algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring opportunities for parallelism of the completion process, highlighting
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transfer, turbulence modeling, and reaction kinetics. Experience with high-performance computing (MPI, parallel simulations) is highly desirable. Demonstrated ability to work independently and
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parallel, a new portfolio of clients is growing with the development of an R&D cluster around the University and a growing number of international partnerships. About the College of Agriculture and
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