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learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms at the intersection of robot learning, control, and AI
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on Macro, Labor and/or Development Economics. The appointment is for 2 years and will begin September 1, 2026, subject to final budget approval and UAE visa acquisition by the prospective post-doctoral
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to contribute to the intellectual property development through patent filings, as well as participating in standardization efforts relevant to wireless technologies, in coordination with academic and industrial
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in the field of Artificial Intelligence. The successful candidates will be responsible for delivering high-quality instruction, mentoring students and contribute to curriculum development. Faculty
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work experience with some quantum machines and development environments like QiSKit and PennyLane are highly appreciated. The selected candidate will work on cutting-edge technologies in an excellent
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on accelerator developments towards the HL-LHC. Expertise in trigger development, performance and optimization and/or the ATLAS computing model is preferred. The selected candidate will work in the ATLAS
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development of cognitive omni-band wireless systems for 6G and beyond. The successful candidate will be jointly supervised by Mahmoud Rasras and Murat Uysal and will work in close collaboration with other
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
management. Develop and validate computational models for monitoring and predicting infrastructure performance. Collaborate with faculty and researchers across SHORES. Disseminate research findings through
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opportunity to shape a dynamic academic division at a pivotal moment in the university’s evolution. The Dean will lead a vibrant community of scholars who advance foundational and applied research on some of
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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