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. The selected candidate will embark on a research journey within a supportive and resource-rich environment, characterized by state-of-the-art facilities and a network of leading academics and industry
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into the cilia research field and networking. Access to an international collaborative environment that can be used for a mandatory up to 6 months research mobility. Access to state-of-the-art equipment
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. Opportunities to participate in conferences, symposia, and networking events to share and enhance your research. Your role will be pivotal in driving AI innovation and contributing to a transformative approach to
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loads — EV fleets, residential batteries, smart heat pumps, and data-center clusters — across distribution and transmission networks is critical to unlocking deep decarbonization and maintaining grid
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the MADE (Manufacturing Academy of Denmark) research and innovation network, the PhD stipends will be closely linked to fellow researchers at other Danish universities through both academic and social events
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willingness to collaborate, network, and interact with other researchers, and the ability to disseminate research in seminars, conferences, and teaching classrooms. We expect you to lead and undertake
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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fellowship in Experimental Quantum Physics . The project is part of the research project “Hybrid Quantum Networks”, which is financed by the Danish National Research Foundation. Start date is (expected to be
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interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and
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of opportunity to a variety of careers within the private and public sectors. The program includes the drafting of a PhD thesis, active participation in research networks, PhD courses and knowledge