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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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provides a unique opportunity to work at the intersection of AI and experimental science, combining fundamental algorithmic development with real-world applications in scientific imaging. Due to limitations
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intersection of AI and experimental science, combining fundamental algorithmic development with real-world applications in scientific imaging. Due to limitations in electron dose and scan stability, microscopy
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absorption/fluorescence and scattering experiments at X-ray free electron lasers. Your focus will be to derive new algorithms for interpretation of the scattering data by introducing chemical force-fields via
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and application architectures with implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models, and other ML methods for analyzing and discovering patterns in probability distributions in