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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 hour ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). The Goddard Earth Observing System (GEOS), developed by NASA’s Global Modeling and Assimilation Office
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in person in São Paulo and involves single-cell and spatial transcriptomics analysis, integration of multi-disease datasets, investigation of cell–cell communication, and the development
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Your Job: Healthy brain function relies on dynamic changes at the synapse. The relevant synaptic turnover and plasticity processes span spatial scales from the molecular up to the network level, and
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starts. Preferably, you will also have: Interest in global water issues and earth system modelling; Strong quantitative methodological skills, for instance knowledge of (spatial) data analysis
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) Scale up forest dynamics predictions from stand to landscape level. As the PhorEau model cannot be run in a fully spatially explicit manner at large spatial scales, the PhD candidate will interpolate
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machine learning models. ● Prior experience using spatially distributed hydrologic models, specifically those with snow mass and energy balance components Minimum Qualifications: Requires a minimum of a
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to improve process-based understanding of infiltration dynamics in nature-based stormwater solutions by combining field monitoring, experimental investigations, and modelling. The project focuses on spatially
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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of Minnesota. U-Spatial is a nationally recognized model that provides consulting services and drives a fast-growing need for expertise in Geographic Information Systems (GIS), Geographic Information Science
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. Continuous observations, carried out over long periods, are at the heart of the research conducted at ISTerre. They are essential for understanding, modeling, and anticipating the natural processes visible