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Field
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developing the first observation-driven, AI-ready global river data infrastructure. Reporting to Professor Louise Slater, you will lead research combining large-scale Earth observation data, global hydrography
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Statistical methods are leveraged in many scientific applications to: specify a data collection practice (experimental design), draw conclusions from sparse and noisy data (inference), and assess
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, combining biodiversity surveys, animal movement data, landscape modelling and remote sensing to understand how trees within agricultural landscapes influence wildlife movement and landscape connectivity
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investigate the wildlife connectivity potential of tree-based agricultural systems, combining biodiversity surveys, animal movement data, landscape modelling and remote sensing to understand how trees within
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understanding with language-based reasoning. Process micro-facial expression data more efficiently in computer vision and vision language models. Create a language-guided representation for subtle facial motion
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healthy despite being at high genetic risk of dementia? This project will use cutting-edge genetic and molecular data from large human biobanks to identify molecular factors that help protect against
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minewater systems, with additional model development and customisation undertaken in MATLAB or Python to extend capabilities and explore sensitivity analyses. Experimental data from controlled experiments
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in the literature, with a particular emphasis on the ‘mortar’ method in BISON. Validation against experimental data and benchmarking against other codes reported in the recent OECD ‘Pellet-Cladding
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working with Professor Diane Coyle and Professor Qingyuan Zhao, in a University-wide partnership with KPMG. The project is exploring the role of wellbeing at work. More information about the partnership can
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more information about the project and eligibility please click here Number Of Awards 1 Start Date April 2027 Award Duration 3.5 Years Application Closing Date 15 October 2026 Sponsor Newcastle