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colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection, tissue characterization, and visual guidance of robotic intervention. This is a full time
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vision and machine learning methods for multimodal imaging and real-time analysis in colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection
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methods for multimodal imaging and real-time analysis in colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection, tissue characterization, and
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computational frameworks that combine 4D point cloud data, geospatial analysis, and advanced ML/DL algorithms. Integrate dynamic environmental datasets into immersive and interactive prototypes for scenario
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities
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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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algorithmic graph theory. The purpose of the role is to contribute to the project "Algorithmic meta-classifications for graph containment", working with Professor Matthew Johnson, Dr Barnaby Martin and
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collegiality is at the heart of what we do. The Department’s research structure consists of five Research Groups: Algorithms and Complexity (ACiD) Artificial Intelligence and Human Systems (AIHS) Network