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sparsity and dependence. Specifically, the project explores inference for complex models including SDEs with jumps, fractional noise, and heterogeneous interacting particle systems (such as graphon-based
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closed-loop optimization. Benchmark AI-assisted methods against classical control approaches and assess scalability and robustness under realistic noise conditions. 2. Neural Ansätze for Quantum
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of machine learning/AI. This work will incorporate more realistic models of detector behavior and noise including glitches and non-stationarity in order to make robust detection of new physics. The candidate
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