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revolutionize communication systems for the quantum age. As quantum technologies rapidly advance, CLASSIQUE focuses on a critical challenge: how to evolve classical communication networks to support both
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learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as
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, encouraging creativity, diversity, and teamwork. You will have great opportunity to build strong networks with internationally renowned researchers at DTU and other universities as well as industrial partners
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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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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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profile assessments with feedback, career-focused courses, and networking events with trade unions, employers, and other relevant parties. Learn more about these initiatives at our Career Hub
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, CLASSIQUE focuses on a critical challenge: how to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems. CLASSIQUE will address a
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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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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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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