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Field
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Researchers: Ph.D. in Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent
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at computer work station for long periods of time Standing for long periods of time Risk of back injury from moving/lifting, equipment, or materials Lifting/Carrying up to _50____lbs. Pushing/Pulling up
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. Essential qualifications: PhD in Computer Science, Robotics, Machine Learning, or related fields. Programming skills in Python/C++ and experience with ML frameworks (e.g. PyTorch, TensorFlow, JAX). Strong
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), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and
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of data at high luminosity (after Upgrade-2), participation in coordination of activities in software groups or data analysis groups, coaching of PhD as an auxiliary supervisor on machiner learning, new
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
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., knowledge representation and reasoning) and bottom-up (e.g., machine learning) methods to study the representation of geographic categories and processes. While we welcome applicants from a broad range of
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning