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Field
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develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and
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of the designed algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably Bachelor’s degree in Computer Engineering, Computer
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 4 hours ago
Data Exchange Standard (ADES); (2) modernizing short-term impact monitoring through adaptive search algorithms for systematic ranging (Scout); and (3) refining long-term impact prediction via improved
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methods for eliciting and aggregating safety specifications and risk thresh-olds for AI systems, with a particular focus on mechanisms that provide axiomatic guarantees, are algorithmically tractable, and
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for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries of performance, speed, and precision
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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; proficiency in graph data structures and algorithms for geometric analysis; advanced knowledge of surface representation math; experience in writing geometry translators for complex surface features; excellent
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Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks. Key Responsibilities: Develop signal processing and machine learning
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of specific analysis algorithms of the tool. You will work with the external partners being collaborators of DEIS in various projects; also you will collaborate with other DEIS people assigned to the various
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. Key Accountabilities • Design and develop embedded AI algorithms for appliance profiling using smart meter data • Benchmark performance against state-of-the-art NILM approaches using datasets like