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The University of Surrey is offering a fully funded PhD studentship on the topic of Audio/acoustics machine learning for intelligent sound reproduction, with industrial partner Bang & Olufsen
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One PhD studentship is available in the area of machine learning theory (statistical learning theory and deep learning theory) or theoretical-oriented topics, e.g., trustworthy machine learning
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implementation of position location estimation using Machine Learning (ML) techniques has been proposed as a methodology to improve performance beyond that of existing techniques, particularly in non-line-of-sight
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Machine Learning techniques to this data to extract the essential information contained within these trajectories. This will be achieved through the following steps: Develop tools to efficiently generate a
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Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural
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machine-learning techniques in ST studies. Our approach introduces two innovations: developing sparse Bayesian learning algorithms for efficient small dataset analysis and designing a simulator for
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aureus (Orazi et al. mBio 2019). You will apply state-of-the-art, machine learning methods (deep neural networks and evolutionary algorithms) on big-data from thousands of individual bacterial (Lapinska et
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, more agile solutions. Current machine learning (ML) algorithms identify and predict threats but rely heavily on past datasets, requiring significant updates. Continual learning offers a solution by
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fracture occurrence reported to be 3-fold higher in women than men during military training. This PhD studentship will combine state-of-the-art imaging techniques, mechanical testing and finite element
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-machine manufacturing has experienced significant growth over the past five years, with projections indicating continued expansion. Improved efficiency can be achieved by design and materials optimization