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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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success. Basic Qualifications: A PhD in civil, electrical, environmental, or mechanical engineering, or equivalent. A minimum of 1 years of experience in development and testing of algorithmic tools for non
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discovery with a strong emphasis on domain-driven impact. Develop, optimize, and transition algorithm prototypes to robust implementations Work with ORNL researchers, as well as internal and external project
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partnerships with industry, universities, and other national laboratories Design and implement advanced sensing and controls algorithms for manufacturing Communicate research results through presentations
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Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: PhD in applied
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advanced sensing and controls algorithms for manufacturing Communicate research results through presentations, reports, conference papers, and peer-reviewed journals Deliver ORNL’s mission by aligning
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learning including reinforcement learning, nature inspired algorithms, deep learning, agent based modeling, and natural language processing. We are specifically interested in research focused
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validate these distributed intelligence algorithms, enabling breakthroughs in scientific research across DOE domains. The candidate will collaborate with DOE’s SWARM project (https://swarm-workflows.org
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conditions, identification of vulnerabilities, and development of resilience enhancement strategies. Contribute to the design, development, and implementation of new models, methods, and algorithms
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: Develop electromagnetic transient models for bulk power systems, power electronics-based resouces (e.g. HVdc, solar PV inverters), synchronous generators, loads, etc. Develop simulation algorithms