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: Optimization of Innovative Air Mobility Networks Operating Supervisor: Prof. Dr.-Ing Hartmut Fricke, Chair of Air Transport Technology and Logistics and co-supervised by at least one additional
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
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finalization of the foundations of dual-tracer imaging using a GATE-based Monte Carlo simulation Implementation of the developed algorithms within our modular, C++-based and cluster optimized PET image
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, photon flux, and probe shape. Secondly, we wish to use the findings to optimize these parameters, and ultimately, improve ptychographic image quality. For the latter part it is important to compare