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Project details: Adaptive Automated Geological Modelling for Sustainable Mining Applications Sustainable mining requires a proper assessment of the risks involved in any decision in the mining value
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shading or relocation. Addressing this gap requires the integration of physiological models, environmental sensing, and data-driven forecasting, tools that can provide the predictive power urgently needed
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demonstrated in small-scale laboratory studies, its application for hard-rock ore at large-scale is almost entirely unexplored. This PhD aims to bridge this critical knowledge gap by developing novel finite
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The University of Exeter has a number of fully funded EPSRC (Engineering and Physical Sciences Research Council ) Doctoral Landscape Award (EPSRC DLA) studentships for 2026/27 entry. Students will be given sector-leading training and development with outstanding facilities and resources. The...
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statistical and data analysis frameworks are welcomed to be proposed or developed by the candidate as part of the project. We will apply the methodologies to a wide range of data from observations to modelling
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climate models, including the UK Earth System Model (UKESM), resulting in critical gaps in both seasonal forecasts and long-term climate projections. This PhD will develop a new parameterisation of snow
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to other indicators of unrest, such as seismicity. This PhD project will drive innovation in modelling magma-mush processes and the generated surface deformation and seismicity during unrest episodes
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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. CASE Partner Met Office has developed the JULES model that will be used by the PhD student to explore the role of charcoal in soil carbon dynamics. Through various discussions, Met Office staff have
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recombination, maintaining genetic linkage of toxin/antitoxin-like systems. As a result, these chromosomes accumulate deleterious mutations that are unaccounted for in existing gene drive models. The student will