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on holistic understanding from regional net-zero. The PhD project is part of the Regional Energy, Carbon and Land Management initiative, focusing on optimizing energy infrastructure, land use and carbon flows
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research on exciting projects and develop customised products and services for our clients from numerous industries and the public sector. The overarching topics at Fraunhofer ITWM are machine learning
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linkages based on numerical simulations and to transform them into AI- and ML-ready information to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing
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awareness (SA), like our work on Situational Graphs (S-Graphs), improve on existing techniques by combining 3D environmental maps with detailed knowledge about objects into a single, optimized model. First
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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well as cooperation of science and economy. GSaME offers the optimal conditions for graduates to earn the titles of Dr-Ing and Dr rer pol. Young scientists have the opportunity to be integrated
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system performance and guiding the design process. 2. System Optimization for Cost and Performance: Using your numerical model, you will conduct extensive optimization studies. The goal is to fine-tune
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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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with numerical experiments Communicate results through publications and presentations You should have a background in one or more of the following areas: Systems and Control Optimization / Operations