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of research include integrating AI-enhanced weather forecasts with crop models, exploring tools to guide land allocation and nitrogen management, or developing dashboards that fuse multiple data streams
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that can be validated with experiments and bottom-up models at multiple scales in order to predict the macroscopic response. Hence, this research will investigate the degradation of metallic materials under
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in healthcare service and opportunities for identification of such deviations using computer vision approaches. It will demonstrate how deviation data can be used in computer-based simulation models
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– Hereby we offer a PhD Thesis focusing on the topic of Transport Modelling for Sustainable Mobility . Our main goal is to further develop and apply the agent-based simulation framework MATSim. The existing
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environmental, economic, and social sustainability of offshore wind farms (OWFs) across their full lifecycle. The platform will integrate life cycle assessment (LCA), marine ecosystem impact models, and a digital
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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observational research (e.g., simulations) and/or lab or field experiments Experience with the analysis (e.g., sequential analysis, multilevel modelling) and interpretation (e.g., conference presentation
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considered in the current models. whether there are common points in multiple models. This will identify potentially critical points for new models. identify upstream and downstream points not previously
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, with particular focus on modelling and simulation. Some example projects include: "The impact of stellar rotation on the nucleosynthesis in the first generation of stars" "Stripping of planets by