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predictive checking, model comparison) • Computational modelling with Python and Dynesty, JAX, NumPyro, and PyTorch • Use of asteroseismic and spectroscopic survey data (e.g. PLATO, Gaia, APOGEE, TESS) • High
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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Computer Science, Mathematics, or related areas. • Strong background in at least one of the following: formal methods, SMT solving, abstract interpretation, or model checking. • Experience with verification tools
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of contaminated soils on soil strength and erosion control. Developing a Model for the Adoption of Agentic AI in Facilities Management: Enabling Autonomous Decision-Making for Sustainable Built Environments
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Software Development: Building the Next-Generation Trust Maturity Model Integrating DevOps practices into ML-driven systems: A Framework and Maturity Model for Continuous Machine Learning Development
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sensing (e.g., PlanetScope, Sentinel-1), advanced numerical modelling (HEC-RAS, Delft-FM), and targeted field surveys to map mining intensity, simulate channel adjustment, and assess changing flood hazards
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financial economics. You will work at the frontier of interdisciplinary research, using high-resolution flood models alongside property data to build a dynamic picture of where flood hazards are concentrated
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involve experimental optimisation, leveraging computational tools, statistical modelling, and emerging AI/ML applications to streamline and accelerate the workflow for complex mixtures and metabolomics
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, and in collaboration with Previsico Ltd. and the US Geological Survey, the researcher will combine fieldwork, remote sensing, and modelling (using CAESAR-Lisflood) to quantify how burned landscapes
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more frequent and intense extreme rainfall events, creating serious challenges for flood risk management across the UK. Current rainfall datasets are not fit for purpose: radar estimates can be