41 computational-physics-"https:"-"https:"-"https:"-"IFM" Postdoctoral positions in United Arab Emirates
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8 Jan 2026 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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of the advertised topics, as well as an excellent academic record. Candidates with PhDs in Physics or Computer Science may also be considered if they willing to collaborate with mathematicians on these topics
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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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, Computer Science, Engineering, or a related field The terms of employment are very competitive and include housing and educational subsidies for children. Applications will be accepted immediately and candidates
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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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systems to applications in urban science, planning, and decision support. Qualifications Required: PhD in Computer Science or a related field, with a focus in databases, data systems, theory, or algorithms
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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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primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or
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collaboration partners. The Center possesses the unique possibility to investigate cutting-edge interdisciplinary questions within the theoretical and fundamental aspects of Mathematics, Physics, and Computer
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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