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within a multi-disciplinary research environment consisting of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing
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breaking in nature, the limits of nuclear stability, and signatures of new physics beyond the Standard Model. Major Duties/Responsibilities: Develop formalism and methods for computing properties of nuclei
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
in the project. Report and publish scientific results in peer-reviewed journals in a timely manner. Present results at international scientific conferences and meetings. Deliver ORNL’s mission by
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conceptualizing and performing research on the variables associated with nuclear fuel-cycle processes; the fate, transport, dispersion, collection, and measurement of such variables; and the analysis of these data
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, and their interdependencies. Develop processes for transportation route and mode identification and facility sighting, considering natural disaster resiliency, climate change, and energy affordability
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salt breeders) is a plus. Experience in the analysis of heat transfer systems. Working knowledge of computer languages such as C++, FORTRAN, or Python. Experience with computer aided design (CAD
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methods to work with a team of scientists in CSD to model chemical reactions important to determine the longevity of amorphous materials. That mechanistic information will be incorporated into process-based
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. Collaborate within a multi-disciplinary research environment consisting of computational scientists, computer scientists, experimentalists, engineers, and physicists conducting basic and applied AI/DL research
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by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in Computer Science or related discipline. Demonstrated
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such as quantization, model pruning, approximate attention (linear and sparse) and proposing new mechanisms for tackling speed, accuracy, as well as energy issues, for large language mode (LLM) inferencing