203 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Sheffield
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collaboration experience. Main duties and responsibilities Develop findable, accessible, interoperable, and reusable (FAIR) AI / machine learning software, tools, and workflows to support multiple exploratory
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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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speech models to predict communication decline and intelligibility changes for longitudinal monitoring; Design machine learning methods to model relationships between vocal characteristics and impact on
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Award at the University of Sheffield. Imagine being able to check that a powerful quantum computer has performed a calculation correctly without having to repeat the computation or learn the private data
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undertaking this project will gain expertise in computer vision, machine learning, and human-centred applications of artificial intelligence, while also developing skills in interdisciplinary research
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chemistry, machine learning, movement ecology, RF-engineering, electronics etc). Main duties and responsibilities Devise, develop and test fabrication approaches for the construction of microbatteries in a
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3D embryo images (published and those generated in the Strawbridge Lab). to quantify cell numbers and lineages. A semi-automated pipeline using deep-learning-based segmentation (Cellpose-SAM), machine
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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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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