558 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" uni jobs at University of Sheffield
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Overview The Campaigns and Alumni Relations (CAR) department sustains and grows the relationship between the University and our global community of alumni (former students), donors and friends
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the prediction of failure on modern composite structures. This research will benefit from excellent computing facilities, expertise in computer-aided engineering (CA2M lab), the available experimental facilities
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acquire new skills during their time in the role. The School of Biosciences at the University of Sheffield has state of the art facilities, including the Wolfson light microscopy facility. The wider
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data acquisition. • Computational techniques, including machine learning and statistical inference. • Collaborative research at the interface of mathematics, biology, and physics. Why us? The
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physical systems. You will explore how the dynamic behaviour of nanomagnetic devices can be used to realise these KAN functions directly in hardware. Working with a combination of modelling, machine learning
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AI-based diagnostics for fleet-based condition monitoring of electric vehicle motors using machine learning frameworks (S3.5-ELE-Panagiotou)
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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group)
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computer-based models of manufacturing process, allowing for analysis, optimisation and visualisation of operations before physical implementation. It is a sought after skill in many high-value manufacturing
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developing a computational model that simulates blood flow for ICH patients. The research will exploit a powerful new approach — physics- informed neural networks (PINNs) — that combines machine learning with
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Digital and sensor based conformance validation for large scale forged components (C4-AMR-Crawforth)
intermediary data streams that can offer insight into how the component and manufacturing process is performing. Within both of the fields of forging and machining there are numerous industry-ready low-intrusive