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, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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the RTG. This PhD project aims to develop a multi-physical simulation framework for various localization sensors (such as e.g., GPS, IMU, SLAM, RTK) that determines the vehicle’s position, explicitly
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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courses in October. Competitive stipends are available! Please visit our website for the latest information on financial support for PhD positions and the application process. On the site, you can also find
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Infrastructure? No Offer Description Work group: PGI-14 - Neuromorphic Compute Nodes Area of research: PHD Thesis Job description: Your Job: You will participate in an international team in an EU-funded Doctoral
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Infrastructure? No Offer Description Work group: PGI-14 - Neuromorphic Compute Nodes Area of research: PHD Thesis Job description: Your Job: You will participate in an international team in an EU-funded Doctoral
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multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to optimize the design of hybrid electrical–optical computing architectures
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning