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building models (impervious and with openings) capturing loads and pressures on various structural elements, both horizontally and vertically. Integrating experiments with numerical simulations (e.g. CFD
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, matrix composition, and (anisotropic) matrix architecture are influenced by the mechanical and geometric properties of their environment. These computational models can provide crucial mechanistic insights
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6G and beyond. The project combines analytical modelling, system simulation, photonic integrated circuit design, machine learning, and experimental validation using state-of-the-art communication
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—including structured households’ surveys and Agent-Based Modeling (ABM)—for the quantification of adaptation effectiveness and limits. The overall outcomes of the ADELE project will contribute to the ongoing
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, including efficiency, fairness, reliability, and sustainability. Despite recent advances, existing mathematical models often fail to jointly capture these aspects and trade-offs, limiting their ability
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are looking for a motivated and talented PhD candidate to join a unique interdisciplinary project at the intersection of machine learning and formal methods. Information Machine learning models deployed in real
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PhD in Optical Characterization and Photonic Performance Analysis of Liquid Crystal Polymer Coatings
under external stimuli. This PhD project will focus on the advanced optical characterization, modelling-supported interpretation, and performance benchmarking of these coatings. You will quantify how
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pathways and overall process efficiency. Using a combination of model compounds and real lignin-derived feeds, you will investigate the influence of feed composition and operating conditions and apply
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viewing-angle stability; collaborate with experts in optical characterization, modelling, device integration, and perception studies; translate material-level insights into design rules for scalable one-way
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language interfaces to gather and incorporate expert knowledge. A central research question is how human mental models of an optimization problem align or conflict with what optimization algorithms need, and how