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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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Theory, Department of Environmental and Energy Sciences. Become a part of the team and contributing to research on current and future mobility service usage and attitudes among car and non-car owners
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are robotics for mines, construction sites, aerial inspection of aging infrastructure, multi-robotic search and rescue, multi sensorial fusion and multirobot coordination, including multirobot perception
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postdoctoral researcher in textile technology with a focus on pain management using textiles. The project explores so-called MH (machine-to-human) touch as a hypothesis for alleviating chronic pain. By
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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specific research questions: What salient elements in cognitive models represent family forest owners’ perceptions and judgement strategies about forest biodiversity on their ownerships? Do family forest