345 parallel-computing-numerical-methods "Fields Institute" positions at Michigan State University
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that is potentially renewable on an annual basis contingent upon satisfactory performance and availability of funding. The mission of CHaRMNET is to develop novel numerical methods that allow us to
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and critical fields of research. With core funding from Michigan State University, the RDSP provides a committed environment for innovative research. The RDSP Director will be jointly appointed in an
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of the appointment. Preferred Qualifications: Candidates with strong backgrounds in kinetic equation, numerical analysis, scientific computing, PDE together with research experience in scientific
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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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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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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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Desired Qualifications Strong background in biomechanics, biomedical engineering, computational mechanics, or equivalent fields. Strong foundation in numerical methods, especially the finite element
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Science at Michigan State University (MSU) leads an open-rank, inter-college search with the CREATE for STEM Institute, the Program in Mathematics Education (PriME), and the Departments of Computer Science
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python), computational mechanics and fluid dynamics, numerical methods, machine learning, and the finite element method. In this position, the candidate will develop data-driven multiscale/multiphysics