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and algorithms for event-based fusion of two physically-colocalized event-based and depth vision sensors, simulate and analyse these models, and explore possibilities to realize them in CMOS ASICs
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the publications of the consortium. You coordinate the consortium’s quarterly meetings and organise project meetings, workshops and congresses. You attract PhD students and postdocs for fundamental research and
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-constrained platforms, without heavily relying on human interventions for hardware-aware optimization, or are unable to generate runtime-adaptive workloads, such as AI algorithms that evolve over time in
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AI through several shared Chairs across faculties (a Chair on the algorithmic and statistical foundations required to understand AI and its biases, and on today’s various applications of AI [Faculties
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generative AI algorithms and translate them into intuitive explanations. Interest in cyber-physical systems (CPS) and eco-cognition modeling. Good understanding of mathematical optimization techniques
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or PyTorch. Interest in explainable AI and contributing to explainability repositories for trustworthy, auditable algorithms. Experience or interest in cyber-physical systems (CPS), eco-cognition, and
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. You assist with the publications of the consortium. You coordinate the consortium’s quarterly meetings and organise project meetings, workshops and congresses. You attract PhD students and postdocs
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-volume manufacturing Fab or in a corporate technology development Fab (please note that university lab, PhD or postdoc experience would not count in this case). You have direct experience in improving
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integration or development in a high-volume manufacturing Fab or in a corporate technology development Fab (please note that university lab, PhD or postdoc experience would not count in this case). Experience
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manufacturing Fab or in a corporate technology development Fab (please note that university lab, PhD or postdoc experience would not count in this case). You have direct experience in improving performance of N28