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Field
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downscaling models, and working with gridded weather and climate data in NetCDF formats, along with strong scientific communication skills. Interested candidates should submit a resume and cover lever online
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commonly used in earth/environmental sciences (e.g., netCDF). Experience building data pipelines for ingesting and harmonizing data from multiple sources and tracking data provenance. Familiarity with
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their native formats (e.g., NetCDF) is required. Experience with at least one of the following is required: hydrological modelling; land surface processes; climate data analysis; training neural networks
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, Grundkenntnisse in der Datenanalyse (Python, Shell-Skripte, FORTRAN und/oder vergleichbare Sprachen), vorzugsweise für verschiedene Datenformate (z. B. netCDF, ASCII) Sie haben erste Erfahrungen in
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any of the following: parallel computing (MPI, OpenMP, POSIX threads), scientific file formats (HDF5, NetCDF), XML, GUI development (QT, GTK, Glade), version control systems, defect tracking systems
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, OpenMP, POSIX threads), scientific file formats (HDF5, NetCDF), XML, GUI development (QT, GTK, Glade), version control systems, defect tracking systems. Current or recent eligibility for access
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, gridded hydrologic and environmental datasets (e.g., NetCDF, Xarray) Experience, or strong willingness to learn, working with large global models and high-performance computing Strong oral and written
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 14 days ago
visualization and statistical analysis. Experience in handling large scientific datasets in formats such as NetCDF or HDF5. A strong understanding of atmospheric chemistry and transport processes. Strong verbal
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. Experience working with large-scale scientific datasets and formats such as NetCDF and HDF5. Experience applying AI/ML methods to climate, atmospheric, or earth system science problems. Experience with climate
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on reproducibility and open-source best practices. Demonstrated experience in geospatial data analysis and the management of large, gridded meteorological or environmental datasets (e.g., NetCDF