74 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at National Renewable Energy Laboratory NREL
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development opportunities, 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
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to use various computer software programs. Researcher III Relevant PhD. Or, relevant Master's Degree and 3 or more years of experience . Or, relevant Bachelor's Degree and 5 or more years of experience
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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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for learning are critical in this role. This is an excellent opportunity for a candidate invested in the NLR mission who desires relevant laboratory experience in preparation for graduate school or a future
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reactor-scale simulations Knowledge of or experience with catalytic processes (modeling or experimental) Knowledge/experience with machine learning i.e. tensor flow/PyTorch . Job Application Submission
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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