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Field
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(GNNs) is a plus Experience with modeling and simulation techniques, such as: Network, agent‑based, or discrete‑event simulation Monte Carlo or stochastic simulation methods Simulation‑in‑the‑loop (SiL
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numerical simulation methods; effective field theories (e.g. SMEFT/HEFT); collider or flavour phenomenology; Monte Carlo event generation and analysis; phenomenological model building; computational and
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contributions to dark matter research. Collaboration members contribute to detector calibration, data acquisition, event reconstruction, background modeling, statistical data analysis, and Monte Carlo simulations
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studies Monte Carlo simulation and/or statistical software or package development (e.g., R, Stata) Multilevel modeling (MLM), difference-in-differences (DID), or comparative interrupted time series (CITS
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computational tools to predict materials properties at the quantum level. In addition, electronic structure methods that go beyond the accuracy of DFT such as Quantum Monte Carlo, GW, and other advanced
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simulation techniques, including density functional theory (DFT), molecular dynamics, Monte Carlo methods, and free‑energy perturbation calculations. Develop and implement novel computational methodologies and
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] - Dynamical Mean-Field Theory (DMFT) and Density Functional Theory (DFT) - Density Matrix Embedding Theory (DMET) - Density Matrix Renormalization Group (DMRG) - Quantum Monte Carlo (QMC) - Tensor network
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approximations to solution of PDEs Information Geometry theories Monte Carlo Sampling methods. Candidates with strong background and track record in general computational and applied math or PDE analysis will also