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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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, 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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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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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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. Below, intended research objectives are listed, but the list is not exclusive. We expect the qualified candidate to further define the plan for the PhD within the first three months of employment
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Development/upgrading solid oxide cell test setups for advanced electrochemical characterisation, including development of metal test houses Development of dedicated circuitry and analysis software
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patient-based format to address ethics, patient perspectives, and costs, clearly defining societal benefits and pitfalls. Project description We are looking for applicants for a PhD scholarship for the
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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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horizons for advanced technologies. Responsibilities and qualifications The PhD project will focus on the design, synthesis, and characterization of new types of metal-organic network structures, which
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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