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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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characterization of HPC and scientific AI applications or libraries on multi-tier HPC storage systems. Design and evaluation of approaches for time-sensitive or data-intensive processing of data originating
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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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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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behavior, and materials performance under irradiation, thermal, and mechanical loading. Plan and perform thermophysical property measurements and integrate measurement results with modeling predictions
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changing needs Preferred Qualifications Experience in radiological risk assessment Experience in biokinetic model development Experience with Monte Carlo radiation transport software and applications
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single node between multiple secure workloads. Investigate and evaluate mechanisms for secure encrypted communication across RDMA based networks. Design and evaluate key distribution and management
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/Responsibilities: Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions. Design and implement
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team of scientists to develop in situ monitoring technologies for DED-based AM processes. Design, integrate, and deploy multi modal sensor systems (including but not limited to optical, thermal, acoustic
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properties. Experience operating and maintaining Quantum Design instruments. Familiarity with the health and safety hazards associated with an experimental condensed matter laboratory that houses an extensive