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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
CI technologies, hardware and software projects. Works on algorithm development, optimization, programming, performance analysis and / or benchmarking assignments of moderate scope where the tasks
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understand the etiology and pathology of systemic diseases, Digital Twin Innovation Hub is developing technology capable of integrating multiple mathematical approaches to fully represent and predict
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, path finding and routing algorithms, sense of direction, human computer interaction, cognitive navigation, intelligent mobility, and artificial intelligence. Sensor fusion and Signals of Opportunity We
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research has been funded by multiple NIH R01 grants and industry grants with awards from AAPM and ASTRO. Dr. Ren is a Fellow of AAPM. Details about Dr. Ren’s profile can be found at the following link: https
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) delivers capabilities to government customers for multiple applications, from on-site real-time data-intensive underwater acoustic signal processing to standalone remote field systems. ESL is seeking a
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software is not on the blacklist" (without revealing the exact software). There are multiple aspects of this project. For all aspects, some cryptography background is required. Design and analysis of new
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Science in the Grainger College of Engineering at the University of Illinois invites applications for multiple full-time teaching positions to support the continued expansion of our teaching activity
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techniques with new ground-based and airborne sensor platforms, for shallow subsurface surveys. Emphasis is on deploying field setups that integrate multiple sensors, with specific focus on drone-based
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
Nutrition Research Institute (NRI) is a world-renowned center and a global leader in precision nutrition research. Our on-site facilities include multiple wet labs and clinical facilities as well as a
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available