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expertise in specialised domains where labelled data is scarce. Recent foundation models have transformed computer vision, yet their ability to acquire new expertise remains limited when training data is
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environments, spectral sensing in the visible and near-infrared (NIR) range to estimate sugar and starch content, chlorophyll levels, and plant water status, AI-based computer vision systems to monitor crop
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postdoctoral researcher, you will lead the human-computer interaction side of the project. You will investigate how everyday athletes and coaches currently use tracking and feedback technologies, design and
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vision, yet their ability to acquire new expertise remains limited when training data is scarce or specialised. This project aims to develop the next generation of adaptive visual learning systems by
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environments, spectral sensing in the visible and near-infrared (NIR) range to estimate sugar and starch content, chlorophyll levels, and plant water status, AI-based computer vision systems to monitor crop
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on four areas: DC Systems, Energy Conversion and Storage (DCE&S) Photovoltaic Materials and Devices (PVMD) Intelligent Electrical Power Grids (IEPG) High Voltage Technologies (HVT) The department’s ESP
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, hands-on postdoctoral researcher who is excited to push the boundaries of markerless motion capture and its clinical application. You are eager to combine computer vision, biomechanics, and synthetic data
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eager to combine computer vision, biomechanics, and synthetic data generation to build tools that will shape the future of home-based rehabilitation monitoring. You bring: A PhD in Biomechanical
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academic institutions, medical device manufacturers, pharmaceutical companies and wearable technology developers to integrate and analyse one of the world's largest collections of AF burden data, comprising
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atrial fibrillation (AF) burden as a novel biomarker for precision medicine. AF-B-STEP brings together leading academic institutions, medical device manufacturers, pharmaceutical companies and wearable