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Field
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well as on how firms and other actors develop circular strategies under competing demands, regulatory change, and uncertainty about future resource use. One particularly relevant empirical example is end-of-life
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interactive system design, serious games, or real-time human-machine interfaces. Experience with AI methods such as generative models, reinforcement learning, online/adaptive learning, or uncertainty
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for remote sensing and uncertainty estimation. Candidates must have a strong programming background. Requirements: PhD in Computer Science or a related field with a strong emphasis on machine learning
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projects aim for more accurate, more sustainable, more explainable, and more trustworthy machine learning, with quantified uncertainty. The centre brings together perspectives and methodologies from
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learning, with quantified uncertainty. The centre brings together perspectives and methodologies from statistics, logic, language technology, theoretical computer science, ethics, and machine learning in new
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-machine interfaces. Experience with AI methods such as generative models, reinforcement learning, online/adaptive learning, or uncertainty quantification. Research experience in rehabilitation engineering