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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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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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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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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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design, development, and validation of material, control systems, and algorithms for next-generation soft haptic actuators and experiences. Note that the research involves significant interactions with
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Research scientists, graduate students, and undergraduate students. Applicants should have experience in developing and characterizing sensors, structural health monitoring systems, and self-healing
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Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design and performance analysis, Proven track record
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that amplify human potential. The successful candidate will engage in innovative research projects in ML, focusing on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems