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, machine learning, mobile robotics, process control, sensor processing, machine vision, and/or human machine interaction. This position will require working with external partners, corporations, and sponsors
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worldwide challenges. Our research and development capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science & engineering, physics, chemistry, or a
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in material science, chemical engineering, mechanical engineering, polymer chemistry
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data processing and multigroup cross-section generation tools such as AMPX or NJOY. Experience applying artificial intelligence, machine learning, or surrogate modeling methods to nuclear engineering or
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research environment consisting of computational scientists, computer scientists, experimentalists, and engineers/physicists conducting basic and applied research in support of the Laboratory’s missions
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is part of the Geospatial Science and Engineering Division (GSED) at ORNL. The group conducts cutting edge research and publishes from novel machine learning based solutions to large scale geospatial
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, experimentalists, and engineers/physicists conducting basic and applied research in support of the Laboratory’s missions. Participate in the development of multi-physics simulations with machine learning (ML