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mechanisms. The project will combine experimental studies with advanced signal processing and AI techniques (compressed sensing), aiming to detect early signs of failure and enable predictive maintenance
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, including sequential Monte Carlo methods, Gaussian processes and Bayesian compressed sensing. Applicants from different backgrounds are encouraged to apply depending on the specific nature of the project
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energy-efficient cooling technologies for cryogenic temperatures (<70 K). At such low temperatures, conventional refrigeration technologies based on gas compression become inefficient or impractical, as
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this includes: Wave propagation relevant to space communications, navigation and remote sensing. Wave interaction for active, passive and combined remote sensing of ocean, land and atmosphere
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for entanglement detection and classification. The algorithms are supported by affordable, spatial surface and subsea sensing and a low-power edge computing to collect and compress datasets at-sea, allowing the
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Dependent Tuition Assistance Scholarship (EDTAS) award opportunities! Details available at: https://humanresources.ku.edu/tuition-assistance KU’s excellence is a result of the rich tapestry of experiences
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computing, data analysis and visualization, computational imaging and spectroscopy, AI and compressive sensing, and quantum computing to develop robust solutions that bridge theoretical frameworks with real
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! Details available at: https://humanresources.ku.edu/tuition-assistance . KU’s excellence is a result of the rich tapestry of experiences, perspectives, and backgrounds of our faculty, staff, students, and
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, including Tikhonov regularization [3], Bayesian approaches [4], and compressive sensing or sparse regularization methods [5]. However, with the emergence of Physics-Informed Neural Networks (PINNs), new
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based on gas compression become inefficient or impractical, as suitable compressible gases no longer exist and the required infrastructure becomes prohibitively complex and energy intensive. Solid-state