56 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs at National Renewable Energy Laboratory NREL
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comprehensive hazard-based training programs for Laboratory personnel, translating regulatory directives into impactful learning experiences that support operational resilience and safety culture. Coordinate and
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-native, microservice-based systems that apply machine learning and advanced analytics to real network data, contributing to next-generation autonomous networks. Key Responsibilities Design and implement
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, and a competitive benefits package designed to support your career and well-being. Job Description The AI, Learning, and Intelligent Systems (ALIS) Group in the NLR Computational Science Center (CSC
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Learning Opportunities Gain hands-on experience with utility-grade EMT and RMS modeling tools used in industry and research Learn how data centers impact grid stability and how to model their interactions
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collaborators Engage with small-scale and emerging farmers growing on-site and learn about land access challenges Support additional InSPIRE research activities remotely as needed (e.g., assisting with
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to 12-months. To learn more about the work this team does, please click here Power Systems Operations and Controls | Grid Modernization | NREL . The intern will focus on tackling the renewable integration
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The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and execute research in collaboration with other researchers Perform
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composite fabrication The ability to conduct laboratory experiments with minimal supervision, with the utmost regard to safety is required. Adaptability and an aptitude for learning are critical in this role
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Experience applying ML or data-driven methods to forecasting, behavioral modeling, or pattern recognition Familiarity with ML libraries or frameworks such as scikit-learn, TensorFlow, PyTorch, or similar
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candidate is expected to have a good knowledge of steady-state as well as dynamic modeling of power distribution systems. This will be a 3-month opportunity. To learn more about the work this team does