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access to large-scale biological datasets, high-performance and cloud-based computing resources, and work in close collaboration with experimental biologists and external computational partners
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). Clouds, in particular the frozen phase, bring about the largest challenge and uncertainties
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improve malaria surveillance and control. Given the multidisciplinary nature of the project we welcome applications from candidates who hold a PhD in a range of fields including computer
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. DESIRABLE EXPERIENCE Ecosystem monitoring and vegetation indices. Time-series analysis of environmental or satellite data. Cloud-based geospatial computing and large-scale data synthesis. CONTRACT DETAILS
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Positions Application Deadline 18 May 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Nicolaus Copernicus Astronomical Center of the Polish Academy
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bring strong research expertise and strong technical skills within remote sensing and ecology, notably lidar processing. The successful candidate should have: Required: A PhD degree with a publication
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 2 months ago
representation and speed of computation for GISS climate model, particularly the treatment of cloud inhomogeneity in GCM grid scale (~200 Km) and the improvement of K-distribution parameterization for gaseous