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, with expertise both in theoretical methods and in numerical study, and with a particular focus on the application of quantum information driven tools, such as tensor networks or convex relaxations
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quantum-classical approaches for many-body simulations Tensor network and entanglement-based methods in many-body physics Qualifications A Ph.D. in theoretical nuclear physics, quantum information science
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, quantum information science and related fields (non-equilibrium dynamics, quantum simulation with tensor networks, etc.). A Ph.D. in Physics or a closely related field is required. The initial appointment
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computational tensor network techniques as well as background in quantum information science defined broadly is viewed favorably. Information about Prof. Maghrebi’s research group can be found at: https
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: - Quantum computing with qudits, quantum error correction and fault-tolerance - Quantum optics of trapped ions and Rydberg atom arrays - Numerical tensor network techniques - Topological order and (de
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algorithms, error correction, or many-body quantum systems. o Proficiency in numerical simulations (e.g., tensor networks, quantum circuit modeling). · For Experimentalists: o PhD in atomic/molecular/optical
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functional data analysis, tensor regression, high-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated
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Science and Quantum Computation Tensor Learning Team , Generic Technology Research Group, RIKEN Center for Advanced Intelligence Project (Team Leader: Qibin Zhao) Functional Analytic Learning Team
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Ph.D. in Physics or a closely related field is required. Candidates with a strong background in quantum many-body theory and experiences in quantum Monte Carlo and tensor network methods are encouraged