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Field
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Postdoctoral Research Assistant in uncertainty quantification for Earth-system prediction Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, with some flexibility regarding
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constraints on the evolution of early Earth Coupling biological and abiotic models or working with Earth-system feedbacks High-performance computing and large-scale numerical simulations Experience working
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/Responsibilities: Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions. Design
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of the following areas: Earth system models (ESMs), atmospheric chemistry and aerosols, machine learning for Earth system modeling, land surface processes Experience developing independent research
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Apply now Interested in how geospatial data science and GeoAI can be applied to investigate health system resilience under the rising pressure of polycrisis? Excited about developing spatial models
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multimodal geospatial data (e.g., satellite, in-situ, and social data) to advance understanding of Earth system processes. Publish research results in high-impact journals and present findings at
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skills and the ability to work effectively in a collaborative, multidisciplinary team environment. Preferred Qualifications: Experience with AI/ML approaches for spatiotemporal or Earth system data
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differentiation to the emergence of habitable environments, with applications to both the Solar System and rocky exoplanets. The successful candidate will develop theoretical and numerical models that connect
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natural laboratory to advance predictive mineral system models. Our research will aim to improve the geological understanding of VMS mineral systems to constrain the formation and preservation of these
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Model and Data Homogenization (TMDH) project that aims to integrate NASA Earth observation (EO) products, in situ and field-sensor records, and advanced hydro-geologic models into an operational platform