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to operate in a scalable and decentralized manner while achieving obstacle avoidance using onboard sensing and adapting to changes in dynamic environments. In parallel to the theoretical and algorithmic
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operation will be studied. From a methodological perspective, the above research challenges will be tackled through a mix of theory, algorithm design, and analysis of experimental data, partly collected by
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user studies, develop novel algorithms, build immersive/augmented realities, and validate your solutions in real-world settings. This PhD is ideal for candidates interested in one or more of the
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user studies, develop novel algorithms, build immersive/augmented realities, and validate your solutions in real-world settings. This PhD is ideal for candidates interested in one or more of the
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, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms, memory technologies, and neuromorphic hardware architectures for future
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translate abstract democratic, organisational, and societal values such as algorithmic fairness, transparency, explainability (XAI) into rigorous, quantifiable engineering metrics without sacrificing
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democratic, organisational, and societal values such as algorithmic fairness, transparency, explainability (XAI) into rigorous, quantifiable engineering metrics without sacrificing the general utility of said