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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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of Sentinel-2 fluvial scenes’. Earth Surf. Process. Landforms, 45, 3120–3140. Carbonneau et al 2020) ‘Adopting deep learning methods for airborne RGB fluvial scene classification’. Remote Sensing of Environment
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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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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
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Sand is the world’s most used resource after water and intensive extraction is reshaping major rivers and deltas. This PhD will quantify how sand mining alters globally relevant river channels
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traditional forecasting with IoT offers a cost-effective, long-term solution for continuous environmental monitoring. Central to this approach is the use of self-powered sensing (SPS) nodes, which harvest
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and share knowledge of endangered archaeological sites across eleven African countries using a combination of remote sensing, historical map analysis, records-based research supported by ground
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? What observational facilities and methods will allow investigation of bodies beyond the Solar System, the remote sensing of their atmospheres and the search for signatures of geological and biological
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amplification? 3. How effective are emerging low-cost monitoring techniques (e.g., UAV surveys, citizen science, remote sensing) in detecting and managing sediment retention? By bridging geomorphology
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches