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satisfaction, and the availability of funds. The successful candidate will conduct numerical and theoretical research on quantum computing, AI-accelerated quantum simulation and materials discovery, quantum
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to simulations on quantum simulators and computers, and applications of machine learning, including neural quantum states. Candidates will benefit from state of the art computing resources at the Argonne
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of AI for QC led by Dr. Thorpe in the Department of Chemistry. The research will focus on the development and implementation of AI/ML techniques to extend and accelerate quantum chemical simulations
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Quantum Information Science and Engineering Theory Postdoctoral Associate Yale University: School of Engineering and Applied Science: Applied Physics: Yale Quantum Institute Location New Haven, CT
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stochastic processes, Markov models, dynamical systems, quantum walks, or related mathematical approaches. Experience with computational model fitting, Bayesian inference, simulation, or formal model
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camera technology and multiplexed readout systems for quantum information science applications. In this role, you will join a multidisciplinary team spanning several Argonne divisions and contribute
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science. The research at the QNM-I covers a broad range of foundational and applied investigations on topics including quantum computation, control, measurement, communication, and simulation. The QNM-I is an active
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. Position 1: Attosecond electron dynamics This position is part of the DOE-funded Early Career project "Rigorous quantum simulation tools for correlated attosecond electron dynamics in molecules." It will
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approaches for the evaluation and validation of quantum simulations of quantum materials, including realistic treatment of uncertatinty and systematic error across experimental, classical computing, and
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architectural simulators and performance models for quantum, HPC, heterogeneous, or other emerging computing systems. Knowledge of Quantum-HPC architectures and hybrid quantum-classical workflows, including