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artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine learning, high
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of experimental apparatus. Experience with vacuum
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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, computer science, or engineering within the past 5 years. Previous theoretical and/or computational research experience in tensor networks, Monte Carlo, machine learning or a related field Proficiency in quantum
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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal