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on recent advances in recombinant RNAP production, cryo-EM structural elucidation, and fragment-based screening, the project will integrate fluorine-based NMR spectroscopy with active learning algorithms and
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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This PhD is aligned with an exciting new multi-centre research programme on parallel mesh generation for advancing cutting-edge high
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using cutting-edge computational techniques, including machine learning algorithms. Work collaboratively with an interdisciplinary and international team to refine and validate regional wave and ocean
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of this research programme. The project will be the first of its kind to explore the validity of FIFA-approved skeletal tracking systems for the potential application in on-field gait analysis with the possibility
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to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will develop a novel hierarchical control framework
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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project aims to develop state-of-the-art computational methods to optimise the quality of doubly curved shell structures manufactured from recycled, short-fibre composites. A particular novelty of the
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identification context, while promising for network-level monitoring, has been largely underexplored. To this end, the project will explore the application of the next generation of deep learning algorithms, e.g
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in