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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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for Embedded Machine Learning Applications," funded by the NorthEast Launchpad Competition. We are seeking a candidate with a strong background in hardware design and the implementation of machine learning
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networks of these devices we will use digital twins; machine learning models trained to predict physical systems but are differentiable. This project will advance the machine learning methods, particularly
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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on research in the fields of machine learning, hydrology, and water resources management at various
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Supervisory Team: Hector Calvo-Pardo; Vahid Yazdanpanah; Tiago Alves (Solar Americas ); Enrico Gerding PhD Supervisor: Hector Calvo-Pardo Project description: Machine learning (ML) holds immense
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Recent years have witnessed significant strides made by machine learning-based computer vision, thus enabling machines to interpret and understand visual information. However, most machine learning