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Field
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projects in AI-driven GNC for space robotics systems, leveraging both classical optimization techniques and modern machine learning methods. Lead and support research projects in AI-driven solutions for SDA
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research projects in AI-driven GNC for space robotics systems, leveraging both classical optimization techniques and modern machine learning methods. Lead and support research projects in AI-driven solutions
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, CT, and other imaging techniques. Design and optimize multiparametric models to analyze complex imaging datasets and extract clinically relevant features. Develop and optimize newer clinically relevant
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
of swarms are the additional complexity in coordinating the multiple elements. Recent advancements in swarm and constellation optimization have made the concept more tractable, and ripe for exploration as a
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
-body or highly nonlinear systems. The emphasis will be on how AI methods can both characterize and optimize trajectories within complex design spaces. Within this context, contingency planning
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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system optimization for remote construction. This position will focus on advancing research in construction assembly science and technology, logistics optimization, and real-time communication frameworks
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, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
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probes. The postdoc will play a key role in designing and optimizing imaging devices, integrating hardware and software, and collaborating with a multidisciplinary team of clinicians, engineers, and basic
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning