81 phd-position-in-data-modeling-"Prof"-"Prof" Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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Is the Job related to staff position within a Research Infrastructure? No Offer Description Description We are seeking Post-Doctoral Associates who are passionate about Data Science, with an emphasis
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candidate is self-motivated and hard-working with a PhD in Data Science, Computational Social Science, Computer Science, or Information Science. The position requires experience with at least one of the
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by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Description The Center for Interdisciplinary Data Science and Artificial Intelligence
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experiments, and validation of computational models. Required Qualifications: A successful applicant must have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants
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main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning, artificial intelligence, computer vision, robotics, UAVs, etc. is a plus. Other preferred qualifications
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perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems This position
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25 Sep 2025 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Engineering Researcher Profile Established Researcher (R3) Country United Arab Emirates Application
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30 Sep 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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and numerical models as well as constitutive model calibration and validation based on physical experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering
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Description We invite applications for a postdoctoral position in database systems with a strong theoretical foundation. We are looking for a candidate excited by the challenge of rethinking data