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Field
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edge-cloud networks. Additionally, the project will develop novel approaches to implement an adaptive approach to policy and governance management across distributed networks based on operating context
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 7 days ago
. Description: Icy surfaces and cold volatile environments are ubiquitous across astronomical and planetary landscapes. Dense star-forming regions, protoplanetary disks, and cold molecular clouds serve as
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productive scientific results Preferred Qualifications Equipment Utilized High-performance computing; cloud computing; local workstations. Physical Demands and Work Environment Work will be performed in
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
investigate together the chemical and physical evolution of the interstellar medium and star- and planet-forming regions, from clouds to disks, with links to exoplanets and our Solar System. The start date is
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of water vapor: How does vapor buoyancy affect the momentum balance and large-scale circulations in Earth's and other planetary atmospheres? How does vapor buoyancy regulate clouds and Earth's energy balance
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, maintaining, and documenting open-source software tools or R packages Experience with geospatial analysis, environmental databases, and cloud or high-performance computing workflows Demonstrated record of peer
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 7 days ago
proficiency in scientific programming (Python or R) and deep learning frameworks (PyTorch or TensorFlow) is recommended, alongside experience processing large spatial datasets using cloud or high-performance
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assessment criteria, which will be benefit are: Knwoledge and experience of cloud, monitoring & automation foundation Experience in foundation models and Large Language Models (LLMs). Experience in teaching
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navigation, trajectory planning, and point cloud mapping; and (3) deploy the developed algorithms on a physical mobile robot operating in GPS-denied, low-light, and geometrically repetitive environments
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. Experience with high-performance computing clusters or cloud-based Earth observation platforms (e.g., GEE Python API). Experience with airborne lidar data processing and canopy height modelling. We will place