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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 14 days ago
gradients, and geomorphic transience regulate rock-derived nutrient (P, Ca, Mg, K, and bedrock N) distributions across landscape to cross-basin scales. Agricultural & Agroecosystem Dynamics: Quantifying crop
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understanding of the interactions between communication, sensing, control, and decision-making in autonomous robotic systems. Experience in developing algorithms for robot navigation, path and trajectory planning
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. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
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criteria The candidate must have some experience and a strong knowledge of artificial intelligence (AI) applications of the following fields: nonlinear analysis (gradient flows of probability distributions
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environments. Clean public repositories or released source code from past publications is a strong plus. Algorithmic Breadth: Familiarity with probabilistic machine learning, distributional reinforcement
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award at FOCS 2019. These positions are supported by an ERC Advanced Grant (Distributed Quantum Advantage , 2026–2031) and a QuantERA grant (Quantum Network Algorithms , 2026–2029); in the QuantERA
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed
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developing algorithms for robot navigation, path and trajectory planning, resource allocation, coordination, or distributed decision-making. Knowledge of wireless communication systems, communication-aware
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performance, and data and product quality. You will join the section in charge of sensor performance, data quality, algorithm development, and calibration and validation (Cal/Val) for the ESA EO missions
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on developing new generative modeling approaches, scalable training algorithms, and foundation model technologies. The role is suited for candidates with a strong machine learning background who are excited