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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | about 6 hours ago
profile measurements of methane, and profile measurements of aerosol/cloud optical properties using the DIfferential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL) techniques
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
. Description: New techniques and instrumentation are developed and applied for remote atmospheric measurements. Current research includes cloud and aerosol studies using space-based lidars such as the Cloud
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 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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) Experience working across the XR system stack, including lower-level components such as sensing, networking, systems optimization, or edge/cloud integration Experience incorporating machine learning
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: Contribute code in Python (and/or C++/JavaScript), work with APIs, and support cloud-based workflows (Google Cloud/Azure experience is a plus). Documentation and communication: Maintain clear technical
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, Open RAN, 6G/NextG); Hands-on experience with cellular network platforms (e.g., O-RAN, srsRAN, OpenAirInterface, Aether/SD-RAN), edge/cloud computing platforms (e.g., Kubernetes, OpenStack), SDN, and/or
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, cybersecurity, privacy-preserving methods, or distributed systems. Experience with cloud platforms, real-time data processing, or IoT data. Prior research experience and interest in publishing. Physical Demands
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evaluation Knowledge in biology and genomics Experience working with large datasets and numerical computing libraries (NumPy, Pandas, SciPy) Familiarity with Linux environments, version control (Git), cloud
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: Position 1: Postdoctoral Associate – Software Engineering & Technical Development Proficiency in Python, geospatial libraries, API design (e.g., FastAPI), and cloud/HPC environments. Experience with
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI