124 computational-physics-superconductor Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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competitive salary and benefits. Research in the SIT-D lab focuses on understanding and modelling consumers' behavior and decision process for sustainable transport modes and transport innovations. Research is
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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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2 Oct 2025 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country United
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) Country United Arab Emirates Application Deadline 29 Oct 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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of interest include: structural 3D printing (metals, concrete, and composites) computational mechanics and structural topology optimization vision-based structural health monitoring autonomous and drone-based
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for physical systems. The postdoc will work on projects focusing on one or more of the following: Robot Learning & Autonomy – Developing algorithms that allow robots to learn (via exploration or imitation) from
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Description The newly established NYUAD Wireless Research Center at New York University Abu Dhabi, seeks to recruit a Post-Doctoral Associate (PDA) who will conduct research on the physical layer
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. The project activities will involve the development of the theory and implementation of the advanced mechanics and numerical models as well as constitutive model calibration and validation based on physical
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Description The newly established NYUAD Wireless Research Center at New York University Abu Dhabi, seeks to recruit a Post-Doctoral Associate (PDA) to engage in cutting-edge research on the physical
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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