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to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated into smaller, faster, more energy efficient and cost-effective hardware
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to quickly quantify the damage to forest plantations after a cyclone or a tropical storm. There is unrealised potential in using multi-modal computer vision methods that synthesis multi-source Earth
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to maximise early outbreak detection. Active intervention: developing decision-making algorithms that recommend effective public-health interventions. Reinforcement learning (RL) provides a natural framework
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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high dynamic range (HDR) imaging is redefining the way smartphone cameras and displays capture the world. Despite HDR becoming the new standard, many classic image-processing algorithms and generative
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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are poised to re-define our future mobility. However, full autonomy is not possible without all-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms
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June 2026 (self-funded) About the department The Department of Economics at City St George’s, University of London is a leading centre of excellence for research and teaching in economics. The
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automatic registration between different image modalities to guide cardiac interventional procedures. You will work within a multidisciplinary team that includes consultant cardiologists from St. Thomas