140 computational-physics "https:" "https:" "https:" "https:" "LaTIM Brest" Postdoctoral positions in United Arab Emirates
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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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, 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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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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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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rolling basis until the position is filled. A starting date earlier than fall 2026 is also possible. Applicants must hold a PhD degree in Mathematics, statistics or theoretical computer science. The terms
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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