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] Position Title: Position Type: Postdoctoral Position Location: Lemont, Illinois 60439, United States of America [map ] Subject Area: High Energy Physics / Machine Learning Appl Deadline: none (posted 2025
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) , Lattice QCD , Machine Learning , Neutrino Astronomy , Neutrino physics , Nuclear and Many-Body Theory , Nuclear Theory (nucl-th) , Particle Astrophysics , Quantum Field Theory Appl Deadline: 2026/01/02 04
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, towards future colliders. Cutting-edge machine learning developments for classical and quantum computational platforms are pursued in the group to benefit particle physics and beyond. Experience Candidates
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-intensive physics with nuclear/particle aspects, advanced detector R&D, machine learning and AI and emerging computational methods in quantum computing. The position is intended for an excellent and broadly
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algorithms for the next generation of particle physics experiments and also explores other ways AI can accelerate scientific discovery. The group collaborates closely with computer scientists, astrophysicists
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
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Monitoring Drift Chambers (MDT), and in the Event Filter track trigger (EFTracking). The group applies novel Machine Learning tools and techniques to both analyses and trigger upgrades, and leverages FPGA and
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of faculty supervisor, develop novel techniques incorporating machine learning in particle physics event generators. Contribute to the development of machine learning driven techniques in the Pythia 8 event
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The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML) for applications in
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, advanced simulations, and machine learning to study emergent quantum phenomena in platforms such as ultracold atoms, molecules, and photonic devices. Its research develops new frameworks and computational