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accessibility by fostering a respectful workplace - in how we treat one another, work together, and measure success. Basic Qualifications: Phd degree in a related scientific field (e.g. architecture
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, fairness, transparency, and reproducibility. The successful candidate will lead efforts to innovate and evaluate scientific AI models, and software architectures that emphasize the understanding and reducing
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learning, and neural network architectures Strong analytical and problem-solving skills, particularly in addressing challenges related to generative model development and polymer science Excellent written
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developing mathematical tools for engineering or science applications Experience in the design and implementation of scalable numerical algorithms on HPC architectures Applicants cannot have received
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scalable applications for future architectures through the use of proxy or mini applications. • Applying large-scale computational methodologies to meet scientific objectives. • Conduct research and
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and implementation of scalable DL algorithms, (2) design and architecture of integrated, multi-scale, coupled-physics computer codes, and (3) documentation, verification and validation, and software
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implementation of scalable numerical algorithms on HPC architectures Experience developing mathematical tools for engineering or science applications Applicants cannot have received their Ph.D. more than five
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pedestal using high performance computing resources and machine learning accelerated architecture. These simulations will inform integrated modeling for decision-ready tokamak pulse simulation to perform
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scalable modeling and simulation tools for engineering and science applications. We develop and apply new scalable algorithms for advanced computing architectures and use the latest advancements in numerical
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. This position is in the Technology Integration group within NCCS. Specific areas of research interest include: Flexible, composable storage service architectures that expose useful data container abstractions and