130 algorithm-development-"St"-"St" Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems tailored to dynamic, human-centered environments. They may also work with diverse signal modalities, including vision
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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
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invites applications for a Post-Doctoral Associate position, in the area of Quantum Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects
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research on the design and development of next-generation wireless communication systems. Specific topics include, but are not limited to, waveform design for the THz spectrum under hardware constraints
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Responsibilities The PDA will conduct research to design and develop optical wireless communication systems. This involves the development of mathematical models for signal transmission/reception, derivation
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invites applications for a Post-Doctoral Associate position, in the area of Quantum Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects
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, telecommunications or related field. Other requirements include Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design
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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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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