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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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-edge flexibility while incentivizing grid-edge device owners for the power system value of their flexibility and data. These methods will be supported by a novel computing architecture addressing domain
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-edge flexibility while incentivizing grid-edge device owners for the power system value of their flexibility and data. These methods will be supported by a novel computing architecture addressing domain
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. The project is led by Heiko Schütt and will employ one PostDoc and one PhD student. About the role... You will develop new Bayesian methods to compare deep neural network and other artificial representations
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systems based on an analysis of their current architecture and operational data. Machine learning and neural network architectures, including convolutional, recurrent and transformer networks. MLOps and