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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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that influence the development, intensification, and persistence of extreme events, using observational datasets, machine learning, and Earth system modeling. The successful candidate will work with observational
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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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laboratories. The broader goal is to enable AI-driven materials discovery, autonomous synthesis, and the development of high-quality, reusable datasets that support adaptive experimentation and long-term
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of hydrometallurgical processes such as leaching, solvent (liquid-liquid) extraction, and adsorption; evaluating process performance and operability; developing test plans and standard operating procedures; assisting
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across heterogeneous data types such as clinical, imaging, omics, text, and experimental data. The work will include developing approaches for continual model improvement, adaptive federated training
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, battery electric and fuel cell powertrain design, development of supervisory control logic, validation of vehicle models against test data etc. The projects and interests of this group span all modes
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Directed Research and Development (LDRD) project. This research focuses on understanding how critical elements are distributed at mineral-water interfaces, with the goal of revealing the fundamental chemical
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of these systems are insufficient to cover the diversity of human-driven experimental activities. The development of multi-appendage, dexterous robots with embodied intelligence is a key to closing this gap
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental