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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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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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About the role We are looking for a Learning Technologist to join our established digital education team to support the Faculty of Medicine with the configuration of relevant learning technology
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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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We have an opening for a Senior Digital Learning Designer in our innovative Digital Learning Team. The SDLD plays a key role in shaping digital learning within the college, they provide expert
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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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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
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identification framework. The role involves developing a species-agnostic computational pipeline that integrates cutting-edge deep learning methods - such as AlphaFold2 and FoldSeek -to enhance protein function
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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