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worldwide. Research methodology The researcher will combine existing landslide inventories with new remote sensing observations to build multi-year databases of landslide occurrences and control factors (i.e
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role in shaping the research direction by selecting study regions, refining analytical approaches, and integrating methods across atmospheric science, remote sensing, and epidemiology
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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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erosion and subsequent effect on land-to-lake dynamics using isotope tracer and source apportionment methodology at test sites in the Winam Gulf. (2) Explore use of remote sensing data and machine learning
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industries. Using existing plans, remote sensing and laser scanning field-surveys, they will build lab-based digital replicas of UK post-extractive underground and overground landscapes. Then they will import
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Researcher will influence the direction of application areas and algorithm development, receiving direct training in InSAR processing, geospatial data science, and agricultural remote sensing. Co-supervision
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interest, narrowing the scope to natural or cultural sites, and integrating diverse remote sensing datasets. The supervisory team offers interdisciplinary expertise in geospatial analysis, machine learning
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. Working at the interface of climate science, geotechnical engineering, remote sensing and critical asset management you will integrate national datasets (e.g. UK Climate Projections, Environment Agency
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. This PhD project will investigate the interactions between wildfire disturbance and thermokarst dynamics across Siberia and other Arctic regions using multi-sensor satellite remote sensing data provided by
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integrity and develop new ways to assess and monitor these impacts. Working at the interface of climate science, geotechnical engineering, remote sensing and critical asset management you will integrate