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Engineering and Mechatronics. The Proposed PhD thesis topic: “Intelligent Diagnostics of Electrical Machines through AI-Enabled IoT Systems: Design of Custom Embedded Hardware and Protocol-Aware Architectures
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Digital-Twin Technology to Accelerate Development of Electric Propulsion Systems This exciting opportunity is based within the Power Electronics, Machine and Control Research Institute at Faculty
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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our
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of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art
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PhD in Electrical and Electronic Engineering: Advanced Protection Strategies for Low Voltage Networks with High Penetration of Power Electronic Devices (Prot-LVNet) Award Summary 100% fees covered
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: 30,00 | Classification CBA: §48 VwGr. B1 Grundstufe (praedoc) Limited contract until: 30.04.2029 Job ID: 5311 The work group Machine Learning with Graphs of the subunit Data Mining and Machine
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challenging set of design and reliability constraints for the surrounding electrical and thermal systems. A key concern is that the proximity of power converters to electrical machines makes it likely that the
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Department: Electrical and Electronic Engineering Title: Intelligent Distribution System Operation for Low-Carbon Power Systems Application deadline: All year round Research theme: Power and Energy
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Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods
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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning