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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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productivity while reducing external inputs. In parallel, the lab is expanding efforts to understand microbiome-associated phenotypes that contribute to drought tolerance and soil water retention. This includes
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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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, 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
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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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. Integrate the feed chemistry data being developed in a parallel project. Travel to India to help implement the updated model. This would be as needed and no more than two times per year. Conduct a comparative
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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