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the Geospatial Science and Human Security Division (GSHSD) at ORNL. The group performs artificial intelligence, computer vision, and federated learning research initiatives, with emphasis on large scale geospatial
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completed in the last five years. Hands-on experience with machine learning, process modelling, and industrial data acquisition systems is very valuable. This position may also require access to technology
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Requisition Id 12983 Overview: We are seeking a Postdoctoral Research Associate who will focus on developing and applying machine learning algorithms relevant to autonomous experimental laboratories
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comprised of a multi-disciplinary team of scientists carrying out research to improve process understanding of the global Earth system by developing and applying models, machine learning, and computational
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environments with machine learning, and complex systems simulation research. Job Research Professional Primary Location US-Tennessee-knoxville Organization College Of Nursing Schedule Full-time Campus/Institute
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to application of machine learning and data mining techniques to analyze multiple data modalities related to corneal disease, including imaging, genetic and clinical data. Qualifications PhD in Computer Science
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responsibility of carrying out independent research related to application of machine learning and data mining techniques to analyze multiple data modalities related to corneal disease, including imaging, genetic
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responsibility of carrying out independent research related to application of machine learning and data mining techniques to analyze multiple data modalities related to corneal disease, including imaging, genetic
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with machine learning, particularly as applied to hydrology or other environmental systems Experience developing software for, and running software, in a cluster computing environment Experience with
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education. The chosen candidate is expected to develop and execute research projects on applying advanced machine learning algorithms to applications of second life use of electrochemical energy storage