119 parallel-computing-"Institute-for-Basic-Science" Fellowship positions at Michigan State University
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environments, and high-performance or parallel computing is desirable. The candidate will also prepare technical documentation, reports, peer-reviewed publications, and presentations and collaborate
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, dynamic-link libraries, SQL, Git or other version-control systems, Linux, high-performance computing, parallel computing, or automated testing Experience with watershed processes, groundwater-surface
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Position Summary The Topp Lab at Michigan State University is recruiting multiple postdoctoral researchers across complementary areas of root biology, phenomics, and translational crop science. The
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. The Topp Lab maintains affiliations across Plant, Soil and Microbial Sciences, Plant Biology, and the Plant Resilience Institute, providing extensive opportunities for interdisciplinary collaboration
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campus culture, and abundant academic resources. It has a strong academic reputation in applied mathematics, computational science, and mathematical physics—making it an ideal place for academic
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, agentic multi-modal data science for complex transport phenomena, in addition to forward/backward uncertainty quantification, numerical analysis, and high-performance computing. Equal Employment
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. Significant experience using Python for high performance applications with libraries such as: Numpy, Numba, CuPy, and PyQtGraph Significant experience using C++ for numerical computing applications
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, data analysis, and visualization environments/IDEs (e.g., R/R Studio, Python/Spyder/VSCode) Familiarity with GIS methods and cloud-based computing resources (e.g., Google Earth Engine) for large-scale
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Position Summary The postdoctoral scholar will join a collaborative research team in the College of Natural Science and the CREATE for STEM Institute to support research on AI-assisted formative
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other institutions including the U.S. Department of Agriculture and several universities to improve upon existing remotely sensed data, develop new methods for detection of land management practices