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process. Our lab has been conducting large scale web-based experiments using the masked priming paradigm to measure with high precision how different sources of information are taken into account in
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security. Candidates need to be skilled and comfortable with experimental work in cyber security. Although a postdoctoral/research associate candidate will be preferred, this position may also be filled by a
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accomplishments are preferred. Experience with modern sampling techniques such as metadynamics, machine learning; good computer programming skills, and working knowledge of UNIX, phyton, and shell scripting is
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candidate will be involved in cutting-edge research and development in 3D computer vision and machine learning for the digital preservation of cultural heritage. The project focuses on state-of-the-art
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. The newly established CIDSAI’s mission is to produce research in data science and artificial intelligence. The CAMeL lab’s mission is focused on research in Arabic natural language processing and
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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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on advanced modeling of coupled fracture/damage of geomechanical materials when subjected a range of multiphysical loading regimes including hydraulic, thermal, and chemical processes. The direct applications
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the process of completing a PhD, MD/PhD, DPhil or equivalent terminal degree from a recognized institution (no more than 5 years since completing the doctoral degree) Doctoral research in the area of machine
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initial phase will employ machine learning models, computer vision algorithms, and real-time sensory integration to prototype tools such as SafeCross for safe street crossing and EasyPath for indoor
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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