570 machine-learning "https:" "https:" "https:" "https:" "https:" "Cardiff University" positions at University of Sheffield
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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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experience Essential Application/interview Highly computer literate with excellent communication skills (both written and verbal) and adaptability in your approach working with colleagues at all levels
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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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thermal analysis system in one of our Laser Sintering machines in the Advanced Polymer Sintering Laboratory here in Sheffield, which will provide novel insight into thermal effects within the process
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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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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
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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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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)