122 information-security "https:" "https:" "https:" "https:" "https:" "Dr" "Dr" Postdoctoral positions in United Arab Emirates
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accomplishments and interests, and at least 2 letters of recommendation, all in PDF format. Please visit our website at http://nyuad.nyu.edu/en/about/careers/faculty-positions.html for instructions and information
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Island, United Arab Emirates [map ] Subject Area: Interdisciplinary Data Science and Artificial Intelligence Appl Deadline: none (posted 2025/07/31) Position Description: Apply Position Description
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phenomenology related to neutrino physics, Higgs physics, BSM, cosmology and astroparticle physics. The successful candidate is expected to take a leading role in the research projects in particle physics
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at http://nyuad.nyu.edu/en/about/careers/faculty-positions.html for instructions and information on how to apply. If you have any questions, please email: nhh2@nyu.edu . About NYUAD: NYU Abu Dhabi is a
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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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design and fabrication, drug conjugation, and in vitro and in vivo experimentation) are required. Proficiency in English is required. For consideration, applicants need to submit a cover letter
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systems and the environment under the supervision of Dr. Ahmad Bin Thaneya. The post-doctoral associate will lead collaborative efforts in advancing research focusing on the intersection of infrastructure
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complex sociotechnical systems Strategic learning and equilibrium-seeking algorithms in transportation networks Game-theoretic approaches to cybersecurity and security games Integration of human behavior
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Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum experiments and quantum algorithms for computational geometry problems. Prior
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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