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Field
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/AI: Apply data-driven methods to construct reduced-order models that bridge analytical theory & high-fidelity simulation data, enabling rapid drag prediction across surface parameter spaces
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-definition spatial transcriptomics data are being generated in Newcastle to study links between cellular senescence and cardiovascular disease. These datasets will serve as a test case for developing
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, including Machine Learning & Artificial Intelligence, Colour & Imaging, Computer Vision, Graphics, Data Science, Health Computing, Computational Biology, Cyber Intelligence and Networks. We collaborate with
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-definition spatial transcriptomics data are being generated in Newcastle to study links between cellular senescence and cardiovascular disease. These datasets will serve as a test case for developing
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Administrative Data Research UK programme (ADR UK) . This studentship is one of a number attached to this programme and one of three linked projects addressing issues related to missing data. Early cancer
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to support environmental recording, monitoring, and public engagement. It also plays an important social role, with links to wellbeing, social connectedness, nature connectedness, and reduced eco
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communication and teamwork skills Desirable Experience with life-history, developmental biology, or behavioural ecology experiments in small invertebrates Experience linking empirical data to modelling or theory
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on cutting edge research linking data science with the problem of AMR. The chance to be part of a diverse, cross-disciplinary team with a strong track record in supporting career progression within the field
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: • Knowledge of fluid dynamics, especially experimental methods in atomisation and sprays. • Programming experience (e.g. Python, MATLAB or similar). • Experience with data analysis, machine learning
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learning approach to translating multi-modal inspection data into remaining useful life predictions; and (3) create a dynamic techno-economic model linking real-time condition assessments to optimal