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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 12 hours ago
opportunity? Please email npp@orau.org Qualifications Candidates should have demonstrated experience with: Developing space-based swarm/constellation optimization algorithms Extensive communications protocol
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developments, algorithmic implementation, and analysis. Experience with electronic structure and dynamics, non-adiabatic molecular dynamics, and coding is strongly selected for. Please seehttps
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 18 hours ago
@orau.org Qualifications Background in engineering, computer science, or a similar field. Background in software tools such as Matlab, Python, and C/C++ Experience developing machine learning algorithms
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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Qualifications: Deep knowledge about how biological neurons have been trained before, and new ideas on how to train them Prior knowledge and experience in encoding and decoding information from neurons Prior
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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, Atmospheric and Oceanic Sciences, Geosciences, Computational Science and Engineering, or a related area is required.The position will involve developing models and algorithms for the evolution of inorganic
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members. Participate in traffic scenario generation project and pedestrian modeling project. Develop sophisticated AI-driven algorithms that create realistic, safety-critical test scenarios for autonomous
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods