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for improving the ability of robots to interact with the surrounding environment and humans during the execution of specific tasks. Project description The fundamental vision of RAI is aiming in closing the gap
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Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute • | Jena, Th ringen | Germany | about 16 hours ago
in English and German language courses. Full-time / part-time full-time Programme duration 6 semesters, 8 semesters Beginning Only for doctoral programmes: any time Application deadline Please see our
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for sustainable life, based on science and education. Through our focus on the interaction between humans, animals and ecosystems and the responsible use of natural resources, we contribute to sustainable societal
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must have a Master's degree in Computer Science or equivalent (with a focus on learning technologies, game-based learning, serious games, human-AI collaboration, or alike). Your course of study must
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-resolution metagenomics and work with population genetics theory. This will cumulate in a new understanding of host-microbial interactions through acclimatization and adaptation, that will be validated in
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doctoral programme is designed to train world-class researchers in Strategic Management and Entrepreneurship, with a strong emphasis on quantitative methods. We combine in-depth knowledge of key topics in
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for improving the ability of robots to interact with the surrounding environment and humans during the execution of specific tasks. Project description The vision of RAI is aiming in closing the gap from theory
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, Antonia Krefeld-Schwalb, Anne Klesse, Bram Van den Bergh) in the domain of reinforcement learning, deep learning, causal inference, field experiments, consumer behavior, human-AI interactions, behaviorial
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crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human
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emotion safety is crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep