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electronic converters are required to connect renewable energy sources and energy storage systems to the power network. These converters employ sophisticated control algorithms that must simultaneously achieve
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consumption, create energy labels for algorithm scalability, and guide implementers in choosing more efficient algorithms. Ready to make AI more sustainable? Apply now! The goal of your PhD project is to
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forcing wind turbines offline or heatwaves and droughts reducing cooling water availability for thermal power generation. Of particular concern are compound energy droughts, where multiple stressors occur
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the Novo Nordisk Foundation, that will drive research and innovations at multiple levels - from developing scalable quantum processor technologies to solutions for the quantum-classical control and readout
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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quantum processors using this technological platform design and implement optimization techniques for full-stack improvement of quantum algorithms model major sources of experimental error for control
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algorithms model major sources of experimental error for control theory or co-design methods Previous works can be found under the bibliographies of Dr. Felix Motzoi and and Dr. Matthias Müller: https
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of structures, facilitating a form-finding process driven by FEM analysis. Training deep learning algorithms to suggest multiple structural concepts tailored to specific boundary conditions. Expanding FEM
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy