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learning algorithms for engineering systems Programming experience in FORTRAN, C, or C++ and scripting experience in Python or similar languages Experience with parallel computing environments and Linux
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, with experience in additional languages such as Fortran considered a plus. Strong knowledge of at least one parallel programming model commonly used in HPC, such as MPI, OpenMP/OpenACC, CUDA, HIP, Kokkos
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part of our research program outlined below. Our lab is in the Department of Biochemistry and Molecular Genetics and part of the RNA Bioscience Initiative at the University of Colorado School of Medicine
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. Experience in developing and applying advanced parametric/machine learning postprocessing techniques, producing probabilistic forecasts of hydrometeorological variables, and parallel computing. Proficiency in
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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protein allowable milk with the least amount of feed and animal inputs under feeding and management conditions in India. • Integrate the feed chemistry data being developed in a parallel project. • Travel
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, Climate Science or a related field. Experience in earth system modeling, data assimilation, and remote sensing of land surface variables. Experience with parallel computing on high performance cluster