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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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facilitate data sharing among actors involved in a new circular flow of flat glass. Within the project, two PhD students, one at the Department of Computer and Information Science (with computer science
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requirements for doctoral studies, you must: hold a Master’s (second-cycle) degree in engineering physics, electrical engineering, machine learning, data science, computer vision, computer science, applied
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. This development has a huge potential for societal impact, with applications in renewable energy, energy storage, electronics, medicine, sustainable manufacturing, etc. The main focus for the advertised position is
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. Within the project, two PhD students, one at the Department of Computer and Information Science (with computer science, or possibly design or cognitive science as main subject) and one at Tema Technology
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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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to assimilate knowledge at the research level. Understanding and experience in machine learning and computer vision. Knowledge, experience, and strong interest and in AI and XR development. Knowledge 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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: 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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pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than 2025-09-18. The Department of Computing Science values gender