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Model measurements, and searches for new physics and performance studies. Candidates with experience in modern AI/ML methods—such as transformer architectures, tokenization strategies, and embeddings
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-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments
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Model, including effective field theories and perturbative QCD, phenomenology at current and future colliders, as well as emerging areas in Artificial Intelligence, Machine Learning, Quantum Information
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the Standard Model, particularly those enabled by novel quantum magnonics technology. Candidates should have interest and expertise in the intersections between HEP, dark matter phenomenology, condensed matter