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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and different applications of EMT simulations. Selection will be based
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frameworks to maintain secure and compliant environments. Document system architectures, processes, and best practices, and contribute to internal knowledge sharing. Participate in on-call rotations and off
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy
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simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor-intensive process because it is shown to be a leading
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simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor-intensive process because it is shown to be a leading
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implementation test capabilities supporting high temperature superconducting magnet development in extreme environments for high field applications of interested to ORNL across different directorates
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Lustre parallel file system. NCCS serves multiple agencies including DOE, NOAA, and the Air Force. The NCCS also supports the center’s Quantum Computing User Program (QCUP) which provides access to state
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science highlights and abstracts from different sources, categorizing them by topical area and working with web staff to incorporate them into a public database. Establish, in consultation with BER staff