17 learning Postdoctoral positions at National Aeronautics and Space Administration (NASA)
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. Description: The Interdisciplinary Consortia for Astrobiology Research (ICAR) project on Advancing Multi-Messenger Biosignature Techniques using Machine Learning seeks suitable applicants to work on any of a
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and related activities. Strong programming skills are a must, including experience with machine learning. Good written and oral communication skills in English are important. Point of Contact Mikeala
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 month ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0202-NPP-MAR26-JPL-HelioSci How to Apply All applications must be submitted in Zintellect Please visit the NASA Postdoctoral Program website for application instructions and requirements: How to Apply | NASA...
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. Description: This project aims to develop a next-generation wildfire risk assessment platform that tightly integrates Earth Observation (EO) data, deep learning, and dynamic fire behavior modeling
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carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
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atmospheric science, physics, and computer science. Experience in analyzing satellite data and geospatial data are desired. Skills in AI, machine learning, deep learning, keras, Pytorch are preferred. A proven
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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to approximate expensive forward and adjoint simulations while preserving underlying physics. Uncertainty-aware inference: combining physics-informed learning for regularization with probabilistic generative
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and machine learning, and for the public to see the worlds of the outer solar as they would appear to our eyes for the first time. The envisaged project includes: image selection, cleaning and