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are uncertain, and decisions about where to survey unfold sequentially under significant time and cost constraints. Existing predictive models provide useful but incomplete support because they cannot fully
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, with a strong interest in the integration of geospatial Artificial Intelligence (AI) and machine learning. Are you enthusiastic about the chance to combine research in Remote Sensing and AI with teaching
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. Existing predictive models provide useful but incomplete support because they cannot fully capture the complexity and heterogeneity of archaeological data. This underscores the need for approaches
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, socioeconomic, and geospatial data; Contribute to patient-level and population-level modelling approaches, including temporal, spatial, and multimodal prediction frameworks; Apply and evaluate methods for disease
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fields: palaeoecological data synthesis and resilience to fire (Dr Jessie Woodbridge), geospatial analysis and archaeology (Prof Ralph Fyfe), palaeo-fire, FTIR and landscape modelling (Dr Michela Mariani
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to open-source releases Your profile - PhD in Meteorology, Physics, Computer Science, Environmental Sciences, or a related field - Experience with atmospheric models and geostatistical interpolation methods
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postdoctoral appointment in Remote Sensing of the land surface, with a strong interest in the integration of geospatial Artificial Intelligence (AI) and machine learning. Are you enthusiastic about the chance to
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transport model (CTM). These geospatial inputs and information will be used to fine-tune a NASA foundation model (Prithvi WxC) to emulate CTM processes, and to predict ground-level air quality data (e.g
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Experience in working with large geospatial datasets (e.g., ERA5, CMIP6, Sentinel, MODIS) Working with complex models and high performance computing, ideally dynamic global vegetation models (DGVMs
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research workflows, and, where possible, public tools or model artifacts. Basic Qualifications PhD (completed or near completion) in one of the following or a closely related field: Computer