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Field
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Overview About the Role Applications are invited for a motivated and committed Machine Learning Engineer. The successful candidate will contribute to Queen Mary’s national reputation for research by planning
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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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generative AI and machine learning to detect code smells and security flaws. We are eager to move forward with the interview process for the Machine Learning position at Haultech Ltd. To better understand your
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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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of an Innovate UK project aiming to develop novel digital health intervention working with industry. This project focuses on the use of machine learning to develop individualised behaviour change interventions
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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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) to work on cutting-edge language assessment projects and help strengthen the British Council's position in the field. The role holder will apply AI and machine learning expertise to develop and support
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to the Power Electronics, Machines and Drives Research Group (PEMC). Your research will focus on electrical machines, drives, design, materials, thermal management, control, and testing. The purpose of the role
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of wireless communications, machine learning, and next generation mobile network architectures. The focus will be on developing novel AI methodologies to add decision-making and automation capabilities across