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application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems, based at the Department of Computer
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested developing new machine learning methods for precision medicine and
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consequences of keel bone deviations: What impact do these have on hen behaviour and wellbeing? high-tech welfare assessment: Help develop a non-invasive computer vision method to track and analyze how hens move
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develops an adaptive AI-guided XR platform for capturing and transferring expert manufacturing knowledge. Your focus will be on developing AI methods for analyzing and modeling human workflows based on data
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: Help develop a non-invasive computer vision method to track and analyze how hens move in 3D space. You will gain hands-on experience in behavioural studies, animal welfare science, and innovative data
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities
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energy, energy storage, electronics, medicine, sustainable manufacturing, etc. The main focus for the advertised position is novel machine learning method development. However, you will be part of a larger
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the fields of cloud computing, computer networking and immersive systems to develop elastic and cost-efficient cloud-based AI pipeline to tackle climate change and support sustainability. Some of the tasks
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the fields of cloud computing, computer networking and immersive systems to develop elastic and cost-efficient cloud-based AI pipeline to tackle climate change and support sustainability. Some of the tasks
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long-term, and most often global, perspectives on future renewable fuels for transport. We seek to rigorously analyse the feasibility of energy transitions, utilize empirical as well as estimated data