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RF front-ends (FR2, FR3, V-band) into end-to-end ISAC demonstrators. Implement and optimize advanced signal processing algorithms for joint communication and sensing. Analyze experimental datasets
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mathematical underpinnings of modern data science and AI. Areas of interest include, but are not limited to: Probability and statistics for data Science and AI Optimization, algorithms, and computational
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are not limited to: Probability and statistics for data Science and AI Optimization, algorithms, and computational mathematics Information theory and learning theory Other foundational areas
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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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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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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infrastructure monitoring, as well as connected autonomous vehicles Integrating multi-modal sensor data with physics-based models Developing robust and adaptive methods for real-time parameter and state estimation
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), Machine Learning on Quantum Computers, Security of Quantum Circuits, Design Automation and Tools for Quantum Circuits, Robust and Efficient Mapping of Quantum Algorithms on Quantum Machines, Quantum Noise
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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