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postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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unconventional sources. The successful candidate will work on continuous hydrometallurgical processing approaches for selective and efficient recovery of critical materials relevant to energy innovation. The
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technical work related to the development of new processes for metals production and processing. Prepare formal reports, make presentations, and publish to establish leadership position in R&D for
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emphasize processing–microstructure–property-performance relationships under temperature, irradiation, corrosion, mechanical stress conditions. The successful candidate will integrate additive
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, machine learning, and signal processing, as well as close collaboration with experts in computational science, electrical engineering, synchrotron physics, soil microbiology, and environmental chemistry
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include processing to structure models of additively manufactured metallic and ceramic materials and structure to performance models based on the crystal plasticity finite element method and similar
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. The successful candidate will develop a novel multiscale multimodal experimental apparatus with precise control during data acquisition, as well as work on data processing pipeline, adaptable to imaging
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focus on the design and development of novel fabrication methods for trilayer superconducting qubits with a particular focus on incorporating atomic scale processing (i.e. atomic layer etching and
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research in electrochemical processing and functional coatings. The primary goal of this work is aimed at advancing material design and surface modification for the electrodeposition of hard-to-deposit