34 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at New York University
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within any of the relevant disciplines of the humanities, social sciences, and computer sciences are welcome to apply. The ideal candidate is a postdoctoral scholar seeking to examine and develop research
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by August 2025. 1. Please upload verification as an additional document in the document upload section. 2. Earned PhD in nursing or health-related field and a strong work ethic and commitment
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grant when extending an offer. Completion of PhD in Data Science, Computer Science, Mathematics, or other discipline aligned with CDS faculty research by the start date. Please follow instructions in
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seeks to recruit a Postdoctoral Associate to work in the field of geotechnical engineering. Required Qualifications: The ideal candidate will hold a PhD in Geotechnical Engineering, Civil Engineering, or
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at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post
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via autophagy and lysosomal targeting in learning and memory and disease models using rodents and iPSC-derived cell cultures. These mechanisms will be investigated in both healthy conditions and
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previous research experience (computational and experimental) in the broad area of Nonlinear Mechanics. Applicants must have received a Ph.D. in Mechanical Engineering, or any closely-related field
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of the research grant when extending an offer. ? Must have a PhD Degree in Engineering, Physics, Chemistry, Materials Science, or a similar field with 5 years of experience. ? Highly proficient in the development
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of virtual reality for research related to human ambulation, balance and gait analysis. The candidate must have prior research experience with virtual reality, computer graphics and animation, gaze detection
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for an individual to be appointed as a postdoctoral associate under the supervision of Prof. Mark Tuckerman. The research will focus on the use of classical and quantum molecular simulation and machine learning