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About the role – In this post, you will join a collaborative BBSRC-funded project focused on using metabolomics and machine learning to predict lameness outcomes in dairy cows. A typical day may
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application of AI and machine learning models to interpret complex X-ray datasets, and the integration of experimental and computational insights to generate actionable knowledge that advances sustainable metal
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particular emphasis on preparing analysis-ready datasets that support downstream statistical and machine learning workflows. You will work closely with Dr Cynthia Sandor within a collaborative and
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delivering an excellent and research-based student experience. They will be engaged in the development of educational thinking, practice and research in the broad areas of curriculum design, learning and
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: Architect and deploy machine learning and computer vision models directly onto onboard edge devices (e.g., NVIDIA Jetson) for real-time object detection, tracking, and autonomous decision-making. Proof
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Engineering, Computer Science, Data Science, Statistics, or equivalent. Strong theoretical background in statistics and machine learning. Knowledge of the basics of federated learning and causal inference is
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, self-supporting, charitable and learned society through the publication of two world-leading scientific journals Biological Reviews and Mathematical Proceedings, an annual programme of public lectures
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project will develop a physics-informed digital twin for offshore wind foundations, combining ultrasonic guided wave monitoring, high-fidelity finite element simulations, Bayesian inference, and machine
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conferences in areas of machine learning, computer vision, and Large Language Models and high-impact specialist peer reviewed academic journals. • Ability to conduct interdisciplinary research activities
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. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model. The EngD