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PhD student to establish recipes to provide post-plasma catalysis with different C2Hy distributions and different enthalpy content for optimization of post-plasma catalysis into C2H4. PhD candidate in
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is to investigate the optimal configuration for utility-scale AWE farms, considering the physics, economics, and environmental impact. To determine the optimal scaling strategies for kite farms, a
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impact of energy generation. This PhD project is part of the MSCA Doctoral Network AWETRAIN (Airborne Wind Energy TRAining for Industrialization Network). Its objective is to investigate the optimal
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environmental footprint.In this context, the optimization of material recovery from end-of-life vehicle scrap is challenged not only by the heterogeneous and evolving nature of this scrap, but also by the diverse
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specifically naval architecture, generative AI is lagging behind. This is largely due to data scarcity in the maritime domain. Unlike media domains where vast datasets are readily available, ship design data is
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specifically naval architecture, generative AI is lagging behind. This is largely due to data scarcity in the maritime domain. Unlike media domains where vast datasets are readily available, ship design data is
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, multiphysics simulations, device implementation, and experimental validation. Key responsibilities: Develop theoretical and numerical models of piezoelectric energy harvesters under heavy-load conditions. Design
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adaptation and learning from their experiences. Using a combination of theory, numerical experiments and precision desktop experiments, we will create 3D materials with self-adapting elastic elements
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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Rotterdam. She is intrigued by the lives and relationships of teenagers, and how parents can optimally contribute to their child's positive development, mental health and well-being, despite their decreasing