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systems that assemble themselves, change shape and acquire useful functions? This project will investigate bio-inspired self-assembly using miniature robotic modules and smart materials. It builds
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the reliability of proposed protective measures. The emphasis can be adapted to the student's background, including machine learning, electrical interfaces, mechanical module design or control
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and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and
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develop specialised technical skills while supporting the university's teaching, learning, and research activities through reliable and efficient IT services Main duties and responsibilities Provide
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, delivering and assessing knowledge elements of our Advanced and Higher apprenticeship programmes using active learning techniques within the classroom. You will be responsible for delivering your own units and
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of hyperpolarised xenon to study gas exchange in the brain. The post doc will be required to acquire pre-clinical data in a multidisciplinary team. Designing MR acquisition protocols and robust data analysis
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: Full-time (35 hours) Line manager: Water Compliance Supervisor Direct reports: None Our website: https://www.sheffield.ac.uk/efm For informal enquiries about this job contact Simon Brinkley, Water
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material; writing and developing assessments; and providing students with high quality feedback as a module lead. Your teaching will engage students and will include active learning. You will have proven
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class learning, teaching and research and that’s why we need you! Do you have a passion for data that helps inspire change? This is an exciting opportunity for a pro-active, technologically savvy and
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website . Our website https://sheffield.ac.uk/ijc For informal enquiries about this job contact Dr Jayne Finlay, Lecturer in Librarianship on [email protected] Next steps in the recruitment