104 parallel-computing "Academic Europe" Postdoctoral 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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analysis, scientific computing, PDE together with research experience in scientific machine learning, are particularly encouraged to apply. Required Application Materials Applications will be reviewed
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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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parallel projects will ensure the development of independent projects to support the long-term career goals of the applicant. Generally, the project will center around leveraging bacterial and mammalian
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, biodiversity), U.S. agricultural and conservation policy, and / or Process-based ecosystem models and their outcomes Knowledge of statistical analysis, and experience using one or more statistical computing
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systems, including embedded and resource constrained systems with various heterogenous computing topologies Additional responsibilities include Assisting with development of GUI software tools to
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independently Confidence with computational biology including use of programming languages such as R, python and some familiarity with high performance computing Excellent communication and interpersonal
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conducting and analyzing molecular dynamics simulations Experience using high-performance computing platforms Experience developing neural network models Experience using AI-assisted coding tools for
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policy, agronomy, or a related field Desired Qualifications Strong skills in satellite remote sensing, GIS, and statistical analysis. Experience using cloud-based computing resources (e.g. Google