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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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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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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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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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in academia, industry and many related careers. Visit http://www.sheffield.ac.uk/sgs to learn more. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply
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your communication abilities and experience the breadth of technologies that are used in academia, industry and many related careers. Visit http://www.sheffield.ac.uk/sgs to learn more. Please apply for
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candidates in the area of Business Analytics, with particular interest in those applying machine learning and deep learning methods to business domains, such as HR analytics, marketing analytics, and
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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