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that dynamically support drivers based on cognitive state, workload, attention, and situational context. This includes designing and evaluating multimodal interaction concepts using visual, auditory, and haptic
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theoretical advances into practical insight and tools that can support analysis, design and decision-making for AI-enabled space systems, thereby bridging the emerging scientific theory of deep learning with
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executed. In close collaboration with PhD researchers and project partners from TUM and ETH, you will contribute to the development of novel control and learning methods for aerial manipulators and multi
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