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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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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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machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap semiconductors present challenges, but it can enable electronic devices with
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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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energy-efficient and sustainable transport systems through world-class research in tribology and machine elements. Friction losses in vehicle systems still account for a significant portion of global
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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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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