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platform. The successful candidate will (1) integrate heterogeneous sensors and onboard computing hardware; (2) develop methods for LiDAR-based simultaneous localization and mapping (SLAM), autonomous
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computational models, systems, and AI/ML tools using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. In
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hybrid classical-quantum computing systems, including familiarity with quantum hardware platforms, quantum-classical interfaces, or co-design of hybrid algorithms and system software stacks. Deep technical
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discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments, HPC in a Linux
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discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments, HPC in a Linux
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discretization techniques for unstructured meshes and/or finite elements with an emphasis on highly scalable algorithms for exascale HPC environments Experience with parallel computing environments, HPC in a Linux
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Computing Methods for Physical Sciences Section in CSED. The MsM group is focused on delivering multiscale, multi-fidelity computational models and systems using algorithms and analytics for materials and
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success. Basic Qualifications: Ph.D. in electrical engineering, power systems, computer engineering, control engineering, or a closely related field. Strong background in power system modeling, dynamic
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algorithms at scale on ORNL's computational resources, including the Frontier supercomputer, addressing critical challenges in science and engineering. Communicate and coordinate experimental results with
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a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex data, and