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Sustain and DTU FOOD), as well as two Departments at NTNU (Chemistry and Biology) will supervise and integrate the PhD fellow into several research communities. There will be an enhanced focus
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integration of model checking and synthesis with machine learning will provide the key to innovative, highly scalable methods for learning, analysis, synthesis and optimization of cyber-physical systems. Based
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, section for Fluid Mechanics, Coastal and Maritime Engineering. The PhD project will play an integral part of the project “Marine advection and dissipation in the vegetated coastal environment”, financed by
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converters are increasingly deployed not only to integrate renewables, but also to control potentially flexible loads such as pumps, fans, EV chargers, and others. With their large energy consumption
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models
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the opportunity to participate and influence the development of advanced power system models for renewable energy integration in collaboration with the top universities and industry in the field
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related programming and hardware circuit skills. This, as well as to research proper methods for educating teachers to apply knowledge, competences, and skills needed to conduct and develop effective and
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such 6-15 GHz into use and developing the needed radio resource management innovations to unleash its full performance potential. This includes integrated scheduling, link adaption, MIMO adaption
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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution