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behaviour in Luxembourg and beyond. EMUP will employ a data-driven approach and state-of-art methodologies to support decision-making for an optimal and smooth integration of the e-mobility sector, with
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garments into recycle, reuse, and manual-review streams; this PhD project tackles the core challenge of designing and optimizing a high-throughput hyperspectral imaging system, fused with complementary
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). This project aims to accelerate the energy transition in Luxembourg by co-creating an ambitious research program. It will utilize a data-driven approach to support decision-making for an optimal energy system
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modeling, optimization, and/or data analytics. Description of the PhD topic (subproject C1 - New metrics for transportation system appraisal): Transportation systems must deliver “function” (i.e
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material configurations, (ii) optimization of material choices and manufacturing processes, (iii) mechanical characterization of the built propellers.The PhD researcher will interact with the different
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of Campylobacter through literature review and conduct interviews to assess the current biocide use in the Danish poultry industry. Develop, optimize, and validate culture-independent assays by planning and
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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the power supply leading to significant enhancement of grid resilience. Collaborative operation of the local energy systems will be studied to leverage differences in storage capacity and renewable energy
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scales and different phases which leads to nonlinear time and history dependent material behavior. Additionally, innovative changes are happening in the steel production process, especially in the drive
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machine learning for safe and optimal control of cyber-physical systems. The projects are expected to be funded by the VILLUM INVESTIGATOR project S4OS (“Scalable analysis and synthesis of safe, secure and