56 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Oak Ridge National Laboratory
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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seeking postdoctoral candidates to investigate the mechanical and thermophysical behavior of irradiated metals and ceramics using advanced experimental and computational methods. The selected candidates
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in ORNL’s Center for Radiation Protection Knowledge (CRPK). The candidate will work with experts in computational radiation dosimetry and risk assessment. The candidate should be an independent thinker
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, reports. Seek membership in professional, academic, and research organizations. Basic Qualifications: A PhD in computer science/engineering or relevant area with an education and a research track record in
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capabilities in a wide range of areas, including applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data