33 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at New York University
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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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in at least one of the following: Social Robotics, human-robot interaction, human-computer interaction, Machine Learning, Affective Computing, Social Signal Processing, Computer Vision and speech
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research team including experts at NYUAD and Cleveland Clinic Abu Dhabi with a demonstrated track record in vision science, neuro-imaging, neuro-ophthalmology and deep learning. 2) Using Advanced Imaging
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technologies, in coordination with academic and industrial collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other
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year of a PhD program who are looking to postpone the formal economics job market and build a strong academic record. The position does not require teaching, but it may be possible to get teaching
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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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-reviewed publications. Prior experience with biological network analysis and practical application of a variety of machine learning and computer vision techniques is preferred. The successful candidate will
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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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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