29 algorithm-"Multiple"-"Prof"-"Simons-Foundation"-"DIFFER" Postdoctoral positions in United States
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
supporting multiple probes simultaneously. Swarms also provide the usual benefits of multi-element array reception, namely robustness to single point failures and transmit/receive diversity. The downside
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Engineering, Aerospace Engineering, or a related field. Degree must be conferred upon hire. Preferred Qualifications Applied expertise in optimal control, heuristic optimization, graph search algorithms, and
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algorithms, and machine learning to solve complex aerospace engineering challenges. Developed and implemented AI-driven solutions for autonomous lunar and asteroid landings, as well as cislunar operations
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large quantities of data to gain a greater understanding of our systems and develop data analytics and artificial intelligence algorithms. You will be actively engaged in the research and development
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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data management sufficient to create, transform and integrate data in a variety of resolutions and formats. Analysis will include running machine learning algorithms (e.g., Random Forest, CART) and
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develop signal processing algorithms to characterize structural health in microreactors and other advanced nuclear reactor technologies. Metrics for success will include scientific output, disseminating
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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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with Neutrons. Jason Fry’s group has worked on significant contributions to Nab and BL3 and supports their experimental efforts through work from the PI and undergraduate students through multiple NSF
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with