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Digital Education. If you're passionate about leveraging digital technology to enhance learning experiences and eager to gain professional experience in a dynamic environment, we invite you to join us as a
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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and then characterise them. You will design, engineer, and characterise proteins with improved nutritional performance and expression, using a combination of machine-learning-assisted protein design tools
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Automatization and Digital Enhancement of Characterisation Techniques: Joining the Dots between AI, Machine Learning and Materials Advances School of Chemical, Materials and Biological Engineering
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Supervisor: Professor Fernanda Duarte Start date: 1st October 2026 Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand
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reduce risk in early-years nursery settings through both proactive prevention and reactive response. About you Degree (1st or 2:1) or equivalent in AI, Computer Vision, Machine Learning, Data Science (ML
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Role: Assistant Professor in Mathematics and Digital Learning Grade and Salary: Grade 7, £37,694 - £47,389 per annum FTE and working pattern: 1FTE, 35hrs per week, Monday – Friday Contract: 24
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary CNC machining delivers high precision but is costly, rigid, and limited in adaptability. Robotic
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This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological prediction. The Research Fellow will develop ML methods for postprocessing numerical ensemble weather