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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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at Master’s level in Computer Science, Artificial Intelligence, Machine Learning or Cybersecurity or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in
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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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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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systems: (1) Event vision for low-latency human / robot co-working scenarios Convolutional neural networks for computer vision require substantial computing resources and introduce significant latencies
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Mathematics,' and 'Computer Vision and Machine Learning' at the Faculty of Engineering, as well as Mathematical Statistics, which is cross-faculty. The position is located at the Division of Mathematical
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cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities for practical impact by taking the outputs from the developed machine learning
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' at the Faculty of Science, 'Algebra, Analysis, and Dynamical Systems,' 'Applied Mathematics,' and 'Computer Vision and Machine Learning' at the Faculty of Engineering, as well as Mathematical
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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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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Computer Science PhD students will work on robot learning