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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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, scikit-learn, TensorFlow, PyTorch). Hands-on experience with data science workflows, including ML/AI model development, training, and evaluation for predictive analytics or decision support. Excellent oral
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
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to analytical techniques for characterizing effluent analytes using LC-MS, GC-MS, IC, ICP-MS, including developing in situ measurement routines aimed at understanding degradation mechanisms of membrane and
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in artificial intelligence and machine learning, particularly for control applications Proficiency in Python Strong analytical and problem-solving skills Ability to work independently and
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or equivalent. Knowledge and experience with analytical techniques such as XRD and SEM. Skill in devising and performing experiments to acquire data, using and maintaining research equipment, compiling
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to analytical techniques for characterizing electrolytes using UV-VIs absorption spectroscopy, ICP-MS, LC-MS, GC-MS, and ICP-MS. This position will include learning experimental workflows and adapting them
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The ALCF has an opening for a postdoctoral position in AI-assisted scientific visualization and intelligent data exploration. The successful candidate will join the Visualization and Data Analytics
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energy supply chain strategies. Apply advanced analytics and methods to analyze trade, production, and geopolitical data to identify risk in critical supply chains. Develop and maintain analytical models
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operations is preferred, working knowledge of machine learning and artificial intelligence methods is highly desirable The successful candidate will demonstrate expertise in accelerator physics, accelerator