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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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stress, and an online learning platform with 100+ different courses; 7 weeks of birth leave (partner leave) with 100% salary; partly paid parental leave; the possibility of setting up a workplace at home
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with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs, recovery of dual-comb measurement signals and
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, stringent layout design rules demand new design automation solutions beyond the actual state-of-the-art. The proposed work plan focuses on the thorough exploration of innovative generative machine learning
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. The project proposes an innovative approach to model sea ice dynamics from the ice floe scale to the basin scale, leveraging hybrid data assimilation and machine learning methods to shape a physically robust
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. The candidate will also collaborate with the Department of Computer Science at Kiel University and the remote sensing company EOMAP GmbH, employing state-of-the-art machine learning techniques to improve
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you have a background in deep learning and computer vision? Are you
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student will become part of a team at DTU with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs
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Do you have a background in deep learning and computer vision? Are you independent, creative and eager to take initiatives? Do you enjoy working in an international research group and interacting
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pathology applications, including the assessment of kidney biopsies. The innovative application of machine learning in clinical settings creates a vibrant and inspiring research environment. You will be part