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photorealistic game worlds. To achieve this goal, we need advances in many areas, from light transport, sampling, geometry and material representations, and computationally efficient algorithms to display
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative
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outcomes. By mapping these gene distributions and integrating them into a predictive tool, the project seeks to stratify patients as likely responders or non-responders to chemotherapy, enabling personalised
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relationship between brain structure, myelin distribution and genetic factors in MS. Research Focus: Recently, computational pipelines have been developed to integrate genetic and imaging databases
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Project summary: A transition to net zero is desirable overall, but its costs and benefits are unevenly distributed across time, space, and between individuals, and policy to implement it must be
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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microbial communities. In this role, you will develop hybrid species distribution models that combine climate and landscape data to predict how microbial taxa niches shift under changing land use and
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aims and objectives This project aims to develop an optimised, fault-tolerant implementation of the Falcon post-quantum digital signature algorithm for spaceborne FPGAs/processors. The key objectives
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: Framework Development: Design and implement a generative deep learning framework for cross-modal integration and analysis, resilient to distribution shifts. Correlation Discovery: Identify interpretable
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(developed by B.J. Evans, O. Hassan and K. Morgan). This solver directly solves the Boltzmann-BGK model equation for the velocity distribution function, which is a fundamental quantity in rarefied gas