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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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questions for research interviews. Conducting a series of semi-structured interviews at UoN. Transcription and analysis of qualitative data. Attending research team meetings. Contributing to publication
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platform. Training the development digital-twin using real-time data from hardware available Electrical power level studies with developed digital twin to identify visible solutions for distribution electric
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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-edge advancements in automated drug discovery through the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning
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piloting an interventional clinical trial at Nottingham University Hospitals NHS Trust. Patients will be randomised to receive either standard of care (which includes a patient information sheet) or standard
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the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be
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is, in general, a challenging problem and one for which there is relatively little data, because experimental chemistry takes time and is expensive. Within our research group, we have a highly
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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which
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advantageous. Eligibility and Application The position is open for both UK home and international candidates. PhD Start date: 1 October 2025 Application deadline: 10 June 2025 For further information please