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Field
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to enable convergence between the IPSL Earth System model and observations (natural and instrumental archives). · Evaluate the mechanisms of internal ocean-atmosphere coupled dynamics and feedbacks in
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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to lead an investigation exploring the ability of recently developed global earth system models to simulate coastal sea level across sub-annual timescales. This work will leverage a suite of coupled models
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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. This project includes collaborations with Sofar Ocean https://www.sofarocean.com/ (link is external) . Additional details on the research team can be found at: https://simpsoba.su.domains/ (link is external
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studies, and modeling work to conduct curiosity- and application-driven research on marine biogeochemistry and ecosystems in a changing environment. YOUR TASKS Conduct research on the ocean carbon cycle
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-position-deep-sea-ecological-modelling Work Location(s) Number of offers available1Company/InstituteInstitut de Ciències del Mar (ICM), CSIC, BarcelonaCountrySpainCityBarcelonaGeofield Contact State/Province
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and fossil diatom datasets from Alaska-Aleutian coastal marshes and statistical and chronological models that allow reconstruction of the magnitude and timing of past relative sea-level (land level
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Offer Description Understand how large offshore wind farms reshape atmospheric and ocean processes, and help advance sustainable offshore energy through observations, high-resolution modelling and coupled
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to the application deadline Experience from sea ice field work or polar expeditions Experience in work with oceanographic or meteorological data and models What you will do Apply, validate and improve algorithms