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Field
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to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be
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resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's computational resources, including the Frontier supercomputer
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imaging. Develop, adapt, and evaluate modern reconstruction algorithms for accelerated and robust qMRI. Collaborate with a multidisciplinary team to translate methods to clinical research studies and
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imaging. Develop, adapt, and evaluate modern reconstruction algorithms for accelerated and robust qMRI. Collaborate with a multidisciplinary team to translate methods to clinical research studies and
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areas: + Quantum computing and quantum algorithms + Solid-state physics, computational materials science, or quantum chemistry + Battery materials modeling Excellent programming skills (e.g., Python) and
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communication with a record of leading and reporting results. Desired Qualifications: Knowledge of quantum computing algorithms. Familiarity with tensor network methods. Experience programming GPUs. Experience
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an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization algorithms Predicting structured output Self-supervised learning
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distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data management and
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oral communication with a record of leading and reporting results. Desired Qualifications: Knowledge of quantum computing algorithms. Familiarity with tensor network methods. Experience programming GPUs
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distributed quantum computing. The center includes other quantum faculty, and conducts a wide range of collaborative quantum research in the areas of quantum computing, quantum algorithms and complexity