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future grid challenges, including widespread integration of DERs. The selected candidate will model, analyze, and control electric distribution and transmission systems, applying state-of-the-art
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the scientific discovery process by quickly extracting useful information from vast quantiles of data, and by providing feedback to the experiment system enabling autonomous control and steering. New fast data
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closely with senior members of the research group. The term of the positions is typically two years, with the possibility to renew for the 3rd year, contingent on the project process and availability
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that can process and learn from multiple data streams in real-time is key to unlocking the full potential of such instruments. The postdoctoral appointee will be responsible for developing such methods
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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The Chemical and Fuel Cycle Technologies division is seeking a Postdoctoral Appointee to join a multidisciplinary team developing electrochemical reactions and processes in molten salt electrolytes