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enable a step change in power conversion, transmission and distribution through power electronics based on new materials. At the heart of such systems are power semiconductor devices. The advantages
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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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, tailored, complex materials such as advanced polymers or polymeric formulations, catalysts, mechatronic devices and software and algorithms. Design, control and modelling and analyses complement
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and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population
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the opportunity to investigate a broad range of research areas with particular focus on understanding the foreseeable adoption of various energy technologies related to e-mobility (e.g., distributed
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networks, making use of intelligent AI-driven control planes. The work will involve: i) (i) development of network probing algorithms for OSNR/GSNR measurement automation; (ii) development of adaptive ML and
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. Measurements on fuel injectors relevant to current design standards have shown significant influence of injector aerodynamics on the dispersed spray distribution and the importance of prefilming fuel flows
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lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four