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Field
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transformation, machine learning, and data analysis applied in logistics and supply chain management; support graduate-level teaching activities for both the Supply Chain Management residential (SCMr) and Supply
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machine learning, topological quantum materials, and/or related areas. The successful candidate will have access to HiPerGator, the fastest university-owned supercomputer in the United States (TOP500
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the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research on the mathematical theory of deep learning as part of the joint
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quantitative and computational background Experience in one or more of the following areas is preferred:CT or other tomographic image reconstruction, medical image analysis, machine learning or deep learning
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an established code (for example VASP, Quantum ESPRESSO). ● Research experience performing molecular dynamics simulations. ● Experience training, fine-tuning, or validating machine-learning interatomic
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fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding track record in conducting research. You
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considered. General interest in responsible AI, coursework, use of machine-learning tools, unpublished projects, or publications only in unrelated venues do not satisfy this requirement. For collaborative
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machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while enabling more
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of complex molecules and reactions. Must have PhD in Physical/Theoretical Chemistry or Artificial Intelligence/Machine Learning or related fields. This position will be initially awarded for one year, and
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incorporate machine learning techniques and has direct applications for array optimization and AMOC transport estimation. Research Environment The position based at the Rosenstiel School of Marine, Atmospheric