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processes causing consecutive landslides will be undertaken. Training The individual joins a team of international experts who will support through training in remote sensing and GIS, field geomorphic
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preferred) Experience with processing and analysing remotely sensed data Experience with GIS and spatial data analytical techniques Experience with carrying out fieldwork in related fields (e.g. Geography
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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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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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supervised by Professor Dan Parsons, Professor Dapeng Yu (Loughborough and Previsico), Dr Quan Le (Loughborough) and Dr Chris Hackney (Newcastle). Working within the FLOOD-CDT, you’ll combine satellite remote