65 application-programming-android-"Prof" Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI in United Arab Emirates
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Description As part of the Electrical Engineering program of the Engineering Division and the Center of Artificial Intelligence and Robotics at NYU Abu Dhabi the group of Prof. Kyriakopoulos seeks
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Reality to elicit users’ preferences for innovative transport systems. Applicants with a background in behavioral analysis and mathematical modelling are encouraged to apply. Terms of employment include
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. Safe and Certified Control for manipulation: Designing control algorithms that ensure passivity, Lyapunov stability, and safety for human-robot collaboration. Qualifications: Applicants must have a PhD
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they plan to contribute to create interdisciplinary connections within the research areas of the center. Questions regarding specifi cs for this position may be directed to Prof. Hisham Sati hsati@nyu.edu
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efforts relevant to wireless technologies, in coordination with academic and industrial collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering
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learning theory to join the research team of Prof. Muhammad Umar B. Niazi. The position focuses on the design and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems
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. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other requirements include: Strong background in communication theory, signal processing
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. To be considered, all applicants must submit Cover letter Curriculum vitae with complete publication list BSc, MSc and PhD transcripts Research statement elaborating how you plan to contribute
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developing new treatments and diagnostics for cardiovascular, neurologic, and metabolic diseases. The successful applicant will join a number of fascinating projects on engineering novel approaches to modulate
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the research team of Prof. M. Umar B. Niazi. The position focuses on the development of digital twins using physics-informed learning approaches, with specific applications to intelligent transportation