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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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or soon-to-be-completed PhD (typically completed within the last 0-5 years) in Electrical Engineering Fundamental understanding of power system modeling, numerical methods and linear control theory
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inverter-based resources for performing real-time simulations in Opal-RT. Develop and prototype advanced control algorithms for grid forming and grid following inverters. Develop and demonstrate
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. This postdoctoral position in the Coherent and Ultrafast X-ray Scattering group in MSD (led by Stephan Hruszkewycz) is supported by the QNext center, which focuses on how to reliably control, store and transmit
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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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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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PhD level with zero to five years of employment experience. Expertise in testing, characterizing, and measuring MEMS devices and designing feedback loops and control algorithms for the precise operation
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, signal processing, x-ray science etc. are encouraged to apply. Position Requirements Required Knowledge, Skills and Experience : PhD in a relevant field completed in the past 5 years or soon to be
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harness the nonequilibrium correlation between structural, charge, and spin/pseudospin degrees of freedom in two-dimensional (2D) materials. The success of this program will lead to new means to control
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engineering controls such as gloveboxes or hoods is desired, but not mandatory. Strong interpersonal, written, and oral communication skills. Ability to model Argonne’s Core Values: Impact, Safety, Respect