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assist in developing quantum reservoir computing algorithms and work using them to model power systems data from, in particular, NLR’s ARIES platform, to explore the limits of quantum reservoir computing
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Science Center has an opening for a graduate student researcher in Mathematical Optimization for large-scale power systems planning. They will deploy developed optimization algorithms on DOE high
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and test Learning-to-Optimize (L2O) algorithms for accelerating large-scale optimization. Perform computational experiments on benchmark power system datasets. Participate in weekly research meetings
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metrics for large-scale, unbalanced distribution networks. Designing and implementing advanced dynamic state estimation algorithms (EKF, UKF, MHE, WLS-based methods) for measurement-constrained distribution
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grid planning. Design and code efficient algorithms for large-scale optimization problems using the Julia programming language and packages such as JuMP.jl. Experience with Xpress and Gurobi are a plus
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systems engineering, electrical engineering, or other relevant areas Have a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications to complex systems
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National Renewable Energy Laboratory NREL | Los Angeles, California | United States | about 20 hours ago
are not limited to: Modeling building energy systems, energy storage systems, and integration with DOE prototypical building models Designing control algorithms and simulation studies under various utility
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: natural language learning, large language models, foundation models, transformer models Have a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications
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, and LLMs. Implement and Impact: Bring your algorithms to life for industry partners, making tangible improvements in learn to optimize domain. Lead and Collaborate: Manage our project GitHub repository
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structures, and algorithms Experience working in Linux-based environments, command line, and networking Demonstrated knowledge and/or experience with AI-driven systems, distributed software, and telecom