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chemical sciences division, manufacturing science division and building and transportation science division. This project will develop materials and processing technologies that will result in a
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, devices, and networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The selected
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: Experience working with manufacturing, materials, and sensor data Experience with real-time, time-series or streaming data systems and edge AI deployment Experience building and maintaining data processing
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the necessary chemistry and processing modifications to meet target alloy properties. Apply advanced characterization and modeling techniques and make fundamental contributions to the field. Interact with other
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such as DeepSpeed, FSDP, Megatron are highly valued. AI for Operations: Building scalable, energy-efficient, trustworthy, and safe AI solutions for operations at the lab and Department of Energy (in general
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modeling techniques and make fundamental contributions to the field. Interact with other researchers, technicians, and students to shape and drive the research agenda. Present and report research results and
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, including automated QC and uncertainty-aware learning from sparse/noisy measurements Build hybrid mechanistic–AI models linking traits to photosynthesis, stomata, hydraulics, and respiration across
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High