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referees (including email addresses), by email to Prof. John Gallagher ([email protected]). Your application should use the Subject line “Trinity College PhD Application – Geospatial Digital Twin” before
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PhD in Bridging Physics and Earth Observation: Geospatial foundation models for real-time flood mapping. Award Summary 100% Home fees covered and a minimum tax-free annual living allowance
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formulate research questions at the intersection of AI, EO and advanced computing Develop and investigate AI methods for multisource EO data, exploring approaches such as geospatial foundation models and
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, experience working with large-scale geospatial datasets and high-performance or cloud computing, and demonstrated expertise in advanced machine learning. Experience leading research projects, managing budgets
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access to extensive national forest datasets and geospatial information, work within an interdisciplinary research environment, and contribute to developing the next generation of AI methods for large
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must have earned a Ph.D. in wildlife science, landscape ecology, geospatial science, waterfowl ecology, or a closely related field. Preferred qualifications include experience with: GPS telemetry and
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data (e.g. instrumental, laboratory, environmental, numerical and categorical data, text, geospatial data or time series); c) Experience in Python programming and in data analysis and machine learning
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for admission. The project would suit a candidate with a background in surveying/geodesy, geomatics, remote sensing, geospatial science, civil/environmental engineering, hydrology, Earth observation
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Change Adaptation (CCA) simulations and activities in urban environments. The PhD project should leverage multi-scale geospatial data (remote sensing/aerial imagery, point clouds, maps, etc.) and GeoAI
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environmental challenges? Join the Department of Physical Geography at Utrecht University as a PhD candidate and help shape the next generation of geospatial modelling. Your job Predicting ecosystem dynamics