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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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apparatus equipped with thermocouples and thermal imaging to simulate realistic runaway events. Top-performing coatings will be validated in situ on live EV cells under controlled runaway conditions. Dr
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would be doing: Process and analyse large-scale calcium imaging datasets from multisensory experiments, including neural responses from visual and auditory cortices recorded over multiple days Apply and
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of their machines is maximised, or machine downtime is minimised. The aim is to develop a smart sensor prototype and demonstrator for condition monitoring of low-speed bearings. The following objectives are defined
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at the Biodiscovery Institute at UoN, a multidisciplinary centre with advanced laboratory and imaging facilities Access to a full range of equipment and laboratory facilities. Inclusive and supportive lab culture with
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. dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield. His research interests include remote sensing image processing, computer vision and machine learning
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Alex Leung (Mechanical Engineering at UCL) will also collaborate. he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect
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advanced computational analysis of large-scale neural recordings. What you would be doing: Process and analyse large-scale calcium imaging datasets from multisensory experiments, including neural responses
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to produce anti-counterfeit markings, dye-free colour images, humidity and chemical sensors, anti-glare coatings and optical filters. This project will develop additive manufacturing of devices with actively
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at higher risk offered PSA blood tests which are not definitive. Our research aims to develop an image-based approach to screening, combining PSA testing with MRI to better identify aggressive cancers