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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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PREFERRED QUALIFICATIONS FOR SENIOR GEOSPATIAL RESEARCH SCIENTIST PhD in geospatial science, geoinformatics, geography, computer science, remote sensing, or related areas with strong geospatial skills Record
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, user workflows, resource allocation models and performance metrics. Innovation & Thought Leadership: Identify and integrate emerging geospatial/HPC technologies (e.g., ML Ops for geospatial data, real
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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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and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and
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positively Experience with multimodal learning, representation learning, self-supervised learning, or foundation models. Experience with geospatial, remote sensing, and citizen science datasets. Experience
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: Department of Biology (Professor Laura Pollock ) Position Summary: Develops and implements species distribution models (SDMs) to predict the geographic distributions of tropical plant species using
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models ranging from baseline approaches to graph neural networks. You will also oversee the open release of project datasets, models, code and documentation. The successful candidate will join Oxford's
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, environmental modelling and geospatial data science. The position is part of Vertical Africa (VERTICAF), an interdisciplinary research project funded by the Swiss National Science Foundation (SNSF). We
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to ensure deep integration between innovation science, system dynamics, spatial modeling, and ecology. The specific focus of this PhD project is on spatial system mapping and decision-support design. You will