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Field
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development of optimal passive mitigation solutions through the enhancement of components such as elastomers. These elements will be selected and optimised to absorb and isolate shock and vibration while
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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collaboration – for instance, in numerous Collaborative Research Centres (also known as CRC or sometimes CRC/TRR) and in application-oriented research assignments. The University of Stuttgart sets up a close
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infrastructure. The research aims to improve the detection of complex environments, anticipate hazards, reduce accidents, and optimize traffic flow, ultimately enabling safer and smarter autonomous driving
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-processed functional materials Use of AI/ML and numerical simulation to optimize the microstructure-properties in the functional materials fabricated by the LPBF process Optimization of microstructure and
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for additively manufactured switched reluctance machines. The project focuses on optimizing the selection control method according to the machine design. Also, the control method will be used as an input
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electrolysis and fuel cells (SOEC and SOFC). By combining numerical modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic
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material parameters for crystal plasticity simulations from experimental data through inverse analysis to establish structure–property linkages based on numerical simulations and to transform them into AI
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and designing sustainable P2X value chains. As a PhD researcher, you will contribute to the new stack designs for high-temperature electrolysis and fuel cells (SOEC and SOFC). By combining numerical
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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