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provable guarantees AI-based cybersecurity: applying learning and AI-assisted techniques to network security, e.g., automata learning from security logs, validation of protocol models, and verified defensive
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indoor air quality, aerosol science, exposure science, and healthy buildings. Projects may combine controlled laboratory experiments, measurements in real-world buildings, data analysis, and modeling
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validation, while establishing protocols for sensor calibration, spatial sampling, quality control and integration with Earth Observation data. Develop spatial, statistical and predictive models to investigate
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advanced computational models; and Related topics in indoor air quality, aerosol science, exposure science, and healthy buildings. Projects may combine controlled laboratory experiments, measurements in real
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habitual control. It will then test how acute stress alters these processes, combining behavioral performance, physiological monitoring, movement-based phenotyping, and advanced statistical and computational
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through proprioception. We develop widely-used open-source tools (e.g., DeepLabCut), train biomechanically realistic embodied agents (e.g., MuscleMimic, Kinesis, Arnold), and build AI-based models
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and