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photonics sensors in the Silicon Photonics (SiPh) platform. The successful applicant will drive an exciting project on the development of smart optical sensors for environmental monitoring in the oil & gas
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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Applying digital twin technology to ITS applications such as traffic management, vehicle routing, and infrastructure monitoring, as well as connected autonomous vehicles Integrating multi-modal sensor data
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, the successful candidate will conduct cutting-edge research in: Adaptive incentive mechanism design for complex sociotechnical systems Strategic learning and equilibrium-seeking algorithms in transportation
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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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particular focus on applications relevant to the Arab world. The successful applicant will join a multidisciplinary research team working at the intersection of machine learning, algorithmic fairness, human
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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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, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms that challenge traditional assumptions in databases, guided by