14 control-engineering "https:" Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or other related department to apply. The successful applicants will design controllers for a variety of
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collaboration. Qualifications: Applicants must have a PhD in Robotics, Control Engineering, Machine Learning, AI, Mechanical or Electrical Engineering, or a closely related field. Strong focus on robot
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. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or other related department to apply. The successful applicants will design controllers for a variety of
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27 Jan 2026 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Engineering Engineering Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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17 Jan 2026 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Engineering Engineering Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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the Job related to staff position within a Research Infrastructure? No Offer Description Description The Division of Engineering and the Center for Interacting Urban Networks (CITIES) at New York
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) diversity, (v) online controlled experiments, and (vi) network science. The ideal candidate is self-motivated and hard-working with a PhD in Data Science, Computational Social Science, Computer
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8 Jan 2026 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Engineering Engineering Chemistry Engineering Researcher Profile Recognised Researcher (R2) Established
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27 Dec 2025 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Engineering Engineering Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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: Ph.D. in Electrical Engineering, Computer Science, Operations Research, Applied Mathematics, or related fields Strong background in game theory, mechanism design, control systems, and/or learning theory