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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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candidates who can evidence their experience with as many of the following areas as possible, preferably at least two: Interdisciplinary mathematical modelling Machine learning and/or other quantitative
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modelling, sophisticated machine learning approaches etc.) and research profiles that build on current strengths of the School , faculty, and university. The University of Surrey is a top-20 UK university
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experience. You will bring experience in: Computer vision and machine learning: developing, training, evaluating, and deploying computer vision models. Data visualisation and analytics: turning complex data
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machine learning, spatial audio and audio-visual AI into groundbreaking creative technology. About you We seek a talented Research Fellow to investigate generative audio AI technology for production
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science and applications. This project aims to develop the required formalism using modern probabilistic and machine-learning approaches, reformulating the problem in terms of conditional probabilities
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integrating machine learning, computational modelling, and experimental validation. The successful candidate will receive training in both computational and experimental biology within a highly collaborative
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learning experiences that prepare graduates to excel in an ever-changing world. We are seeking a new Pro-Vice-Chancellor, Education to play a pivotal role in shaping Surrey's education and student experience
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(csrankings.org) Home to world-leading research in computer vision, AI, multimodal learning, and robotics You will join a vibrant and tightly knit team of more than 10 other researchers working on a range of
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/structural/mechanical engineering with experience and interest in structural dynamics, vibrational analysis, train-track-bridge interaction, signal processing, data science and machine learning. The successful