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) Development of an Augmented Smart Classroom for Personalized Learning (SmartClass) serving as a test-bed for the collection and analysis of students and professors data, leveraging on data analytics and machine
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machine learning, Computer vision, Swarms, Autonomous Robots, hardware security, and Embedded systems development is desired. The successful applicant will work on various projects on robotics and computer
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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faculty and students are at the forefront of the high-tech start-up culture in New York City. The NYU Tandon School of Engineering is deeply committed to excellence in teaching and learning and fosters
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Postdoctoral Associate to investigate the neural mechanisms underlying continual learning in humans. The successful candidate will develop computational models examining the tradeoff between task
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ophthalmological, neuroimaging and behavioral data, and incorporate deep learning methods to facilitate biomarker discovery and enhance predictive power. As a postdoctoral associate you will join a multidisciplinary
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-dimensional probability, concentration and functional inequalities ? Mathematical aspects of machine learning and deep neural networks ? Free Probability aspects of Quantum Information Theory. While excellent
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teaching and learning. Tandon fosters student and faculty innovation and entrepreneurship that make a difference in the world. The Department of Chemical and Biomolecular Engineering at NYU Tandon is home to
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architectures, including terrestrial and non-terrestrial networks Deep learning for wireless communication problems, particularly in areas such as spectrum management, adaptive system design, or cognitive radio