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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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Biological Learning Lab, Department of Engineering, University of Cambridge (https://cbl.eng.cam.ac.uk ) and Nathalie Rochefort at the Centre for Discovery Brain Sciences, University of Edinburgh (https
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on operations. To support development of complex multi-layered systems and to inform rapid decision-making, this project will develop advanced Machine Learning (ML) architectures that learn directly from a
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computer science, with a focus on data management, distributed systems, machine learning, or related fields. Previous coursework or experience (e.g., thesis) in one of these areas is desirable. Proficiency in
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signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, projects in this theme will consider aspects related to: wide area motion
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an ongoing collaboration with researchers from UCL and the University of Edinburgh. For additional information, please contact Dr Xi Wang at [email protected]. When reaching out, kindly use the subject
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required to induce unstable growth, the natural frequency and oscillation dynamics under ultrasonic irradiation, and their jetting collapse dynamics. You will learn how to set up, run, and post-process large
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Summary: This research proposal addresses challenges in the UK's construction industry, focusing on sustainability & safety. It aims to develop machine learning models that combine data from similar