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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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sits at the cutting edge of materials science and solid mechanics, using real-world microstructure data to simulate thermo-mechanical behaviour at the grain scale. Your work will form a key part of a
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the field of Computational Morphodynamics in plants. The work will be within the ERC-funded project RESYDE (https://resydeproject.org ) with the aim of building a virtual flower using multi-level data and
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Applications are invited for three (3) PhD Studentships, based at the Department of Statistical Science, UCL. The positions can involve any topic within the Statistical Science remit. The positions
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ADTKD diagnostics and clinical trials. What you’ll do You will work at the interface of laboratory and data science, and clinical genetics. You will: Establish and characterise the UK ADTKD-MUC1 cohort
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, organic chemistry and biophysics Are desire to explore the biochemical composition and biological activity of GAGs Are confident working with complex data Enjoy learning beyond your current expertise with
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health, and intelligent operations. It is particularly suited to candidates with academic backgrounds in Industrial Engineering, Aerospace Engineering, Management Science, Operations Research, Transport
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atomisers. To ensure high quality measurement data, enabling meaningful correlations, measurements will be performed on a simplified prefilming geometry, with aero and hydrodynamic features representative
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and an Industrial supervisor at Jaguar Land Rover (JLR). It falls within the remit of the Cambridge Engineering Design Centre (EDC), but interactions with other groups are expected, across and beyond
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Overview: As data becomes more accessible, new challenges arise around how best to use it—especially in complex, multi-system environments like aerospace. Ontologies offer a powerful solution by