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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0321-NPP-NOV25-JPL-PlanetSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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development pertaining to chemical and biological detection. You will gain experience with algorithms, data analysis, Deep Learning, Python, pytorch and /or tensorflow, NLP, genetic algorithm, computer vision
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following: language processing model development machine learning algorithm training Application Requirements A complete application consists of: Zintellect Profile Educational and Employment History Essay
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 2 months ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0321-NPP-JUL25-JPL-PlanetSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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using Python and MATLAB. Why should I apply? Under the guidance of a mentor, you will engage in a variety of research activities, including: developing machine learning algorithms for various research
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investigation and developing software algorithms and techniques to support human or machine information interactions for the purpose of information retrieval/dissemination, analysis and/or decision making
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Mexico, and elsewhere), and in relating particle composition to measurements of size distributions, air mass trajectories, etc.; (2) development of algorithms to process complex spectral data and identify
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algorithms) that enable rapid creation of new high-fidelity multi-scale/multi-physics computer models of materials capable of utilizing modern extreme-scale computing environments. The success of multi-scale
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the development of image/signal processing algorithms from a multidisciplinary approach, to include multiple sensor modalities. These multidisciplinary research opportunities incorporate theoretical and
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and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used