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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data
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a member of the ORNL scientific community, you will be expected to commit to ORNL’s Research Code of Conduct. Our full code of conduct and a statement by the Lab Director’s Office can be found here
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are subject to performance and availability of funding. Letters of Recommendation: References are required. Instructions to upload documents to your candidate profile: Login to your account via jobs.ornl.gov
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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funded by the U.S. Department of Energy (DOE) Office of Basic Energy Sciences (BES) in the Materials Sciences and Technology Division (MSTD). The successful candidate will be expected to work effectively
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testing. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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the position title and number referenced in the subject line. Instructions to upload documents to your candidate profile: Login to your account via jobs.ornl.gov View Profile Under the My Documents section
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. Intelligent Sampling for Federated Learning: Investigating frameworks (such as SICKLE) for intelligently sampling cross-facility extreme-scale data to enhance federated learning workflows with platforms like
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Requisition Id 16061 Overview: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges