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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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materials and energy-intensive manufacturing processes. These machines dominate current electric powertrain solutions but pose challenges in terms of resource supply chain, recyclability, and embedded carbon
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about our proposed methodology. In this method, the images taken by each drone will be loaded into the pre-processing unit and then the pre-processed data will be used as the input of the deep learning
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-speed cameras (in a newly renovated lab dedicated to our research group). A significant component of the analysis will include image processing, including data-driven methods and machine learning. You
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PhD Studentship: Towards Sustainable Powertrains: Enabling High Motor Performance without Rare Earth Materials and Energy intensive Manufacturing Processes The University of Nottingham This project
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Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace, mechanical
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mitigation system. Understanding the process of droplet impact and freezing dynamics at high airspeeds, on textured and non-textured surfaces is critical to deciphering the physics behind ice adhesion and
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establishment and growth of trees in newly planted woodland ecosystems. Despite their critical ecological function, the specific roles of ectomycorrhizal fungi in facilitating afforestation processes remain
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paper and grant writing, and thesis and viva preparation. Interviews are likely to be online in mid-late August 2025. Project Description: Deepfakes, i.e., realistic AI-generated images, videos or audio
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the UKRI rate (£19,237 for 2024/25) and tuition fees will be paid. We expect the stipend to increase each year. The operation of safety-critical components is often contingent on the avoidance