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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success Basic Qualifications: A Ph.D. in nuclear or health sciences (such as health physics
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-, p- and f-block radioactive ions of interest in nuclear medicine. This position resides in the Chemical Separations Group in the Chemical Sciences Division, Physical Sciences Directorate (PSD) at Oak
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implementation of constitutive models within commercial and/or open-source finite element software is required. Preferred Qualifications: Demonstrated expertise in multi-physics FE simulations is preferred
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manufacturing technologies and manufacturing systems that could achieve transformative gains in process productivity and enhance manufacturing sustainability Strong analytical capabilities with an experience in
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Science and Technology Division (MSTD), Physical Sciences Directorate (PSD) at ORNL. Major Duties/Responsibilities: Develop and use first principles methods to describe electronic and magnetic structure
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in physics or a related field completed within the last 5 years
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challenges facing the nation. The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics- models and/or physics-aware ML-models that can bridge