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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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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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? If so, then Norland is the place for you. Our TEF Gold-rated award-winning early years undergraduate degree and integrated diploma programme is world-renowned for delivering outstanding teaching, learning
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About us **About King’s Digital ** Our mission is to provide transformative, high-quality digital education, so students can learn, wherever they are. We collaborate with academics and professional
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__Secure-ROLLOUT_TOKENUsed to track user’s interaction with embedded content. Maximum Storage Duration: 180 daysType: HTTP Cookie __Secure-YECStores the user's video player preferences using
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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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Join us in shaping the future of creative education through innovative learning technology. The Library Services Department plays a vital role in supporting learning, teaching and creative practice
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Accessible Tinnitus Notch Noise Therapy via Machine Learning, Acoustic Metamaterials and Additive Manufacturing (with NHS and TinnitusUK) EPSRC Centre for Doctoral Training in Sustainable Sound