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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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-Doctoral Associate to work on a fascinating project focused on the geomechanical modeling for energy applications. The position will be directly supervised by Professor Mostafa Mobasher and will involve
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Description The Center for Interdisciplinary Data Science and Artificial Intelligence (CIDSAI) at NYU Abu Dhabi seeks to recruit a highly motivated researcher to work on topics in the theoretical
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expertise in these areas is highly encouraged. The selected candidate will work on cutting edge technologies in an excellent research environment, with a potential to work with a Quantum Computer through our
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to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work
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models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations and/or experimental
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of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or experimental means. The PDA is expected to actively disseminate results through publications in
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, statistical signal processing, optimization theory, machine learning and artificial intelligence. The candidate is expected to actively participate in experimental work focused on building datasets of channel
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to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement