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quantum systems and quantum networking theory. Experience with numerical modeling of open photonic quantum systems. Experience with scientific computing using Python and/or Julia. Desired qualifications
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Job Description We are seeking candidates for a 3-year PhD project as part of the European Marie Skłodowska-Curie Actions Doctoral Network on “Consumer Energy Demand Flexibility in Electricity Use
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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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software to increase both the accuracy and efficiency of structural digital representation as virtual digital twins, providing high-quality value-added services to the industry. Responsibilities and
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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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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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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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modelling or coupled wind-wave modelling Experience with wind resource assessment and site suitability, including WAsP and WEng software Experience using wind turbine wake models Knowledge of meteorological