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and analysis of dedicated algorithms for training analog circuits directly from data. In this PhD project, you will develop a novel system-theoretic framework for learning in analog circuits and
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reconfiguration operation. Develop and evaluate fast and efficient scheduling algorithms for fast control and reconfiguration of the optical AI compute clusters. Realize a small-scale compute cluster lab testbed
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include theoretical results, mathematical proofs, and computational algorithms, as well as open-source software and validation through simulations and analysis. Experimental demonstration is an option if it
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to accurate sample reconstructions using advanced signal processing and tomographic reconstruction algorithms. With the inclusion of noise the object estimation accuracy will be based on statistical concepts
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problems. These mathematical insights will subsequently be translated into computational algorithms for inversion, uncertainty quantification and experimental design. The research will initially focus
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lead to better-behaved inverse problems. These mathematical insights will subsequently be translated into computational algorithms for inversion, uncertainty quantification and experimental design. The
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, they have the potential to solve certain computational problems far more efficiently than classical machines. Realizing this potential requires entirely new numerical algorithms that combine advances in
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scheduling algorithms for fast control and reconfiguration of the optical AI compute clusters. Realize a small-scale compute cluster lab testbed to demonstrate and evaluate the performance of the innovative
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use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use
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directions include: Using AI to learn parameters and design circuits for quantum optimization algorithms (e.g., beyond QUBO formulations for QAOA) Developing AI-driven methods to discover quantum optimization