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including the development, training, and testing of computer vision and machine learning tools for AI-automated anatomical landmark detection and segmentation of clinical images. Building upon previous work
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EPSRC ReNU+ CDT PhD Studentship: Physics-informed machine learning for deep geothermal systems under uncertainty. Award Summary 100% fees covered, and a minimum tax-free annual living allowance
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part-time positions are available for those committed to application-oriented education. Job Responsibilities Teach and assess at least 3 courses in the following core subjects: Data Analytics & Machine
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computer systems which support learning. Your teaching will be informed by a combination of practical experience and/or research to support high quality learning and teaching, experience of using Virtual
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within physically sensible design spaces avoiding the need to learn every pathological flow scenario and making machine learning both efficient and reliable. The ultimate goal is to retain the fidelity and
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have an opening for a Senior Digital Learning Designer (SDLD) in our innovative Digital Learning Team, supporting the College of Arts, Technology and Environment (CATE). As a team, we are committed
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) for engineered microbial strains. Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways. Integrate multi-omics datasets into computational workflows
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candidate will: Develop genome-scale metabolic models (GEMs) for engineered microbial strains. Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways
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EngD: Physics-based AI for Intelligent Machining: Learning, Optimisation, and Uncertainty in Next-Generation CAM Software (sponsored by DigitalCNC) EPSRC Centre for Doctoral Training in Machining
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machine learning model to rule out heart attacks in the emergency room, which has the potential to translate to large savings for healthcare systems in the world, (2) a computational modelling to assist in