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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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The Chemical Sciences and Engineering Division seeks a Postdoctoral Appointee to conduct research focused on the development of high-energy, long-cycle-life lithium–sulfur batteries employing both
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apply funding from federal agencies (e.g., the Department of Energy and National Science Foundation). A successful candidate should have a solid background in power system engineering, optimization
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financial models. The position will include the analysis of hydropower operation and expansion, optimization and equilibrium, market penetration, and interdependencies. This description documents the general
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, co-simulation, or hardware-relevant environments. Primary Responsibilities: Develop machine learning and AI methods for control, optimization, and cyber-resilient operation of distribution systems, DER
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components. Work closely with a beam diagnostics physicist and controls group engineer to install and optimize diagnostic systems. Develop and integrate mmWave diagnostics equipment for beam position
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) simulations and reduced order modeling of turbulent and reacting flows relevant to advanced propulsion and power generation systems, such as gas turbines and detonation engines. The successful candidate’s
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced