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qualifications Experience in one or more of the following areas is considered a merit: data analysis using ROOT, particle-transport simulations using Monte Carlo codes such as Geant4, FLUKA, or OpenMC, nuclear
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, or computer science. Core competencies: solid background in quantum many-body physics strong programming skills (Python required, Rust a plus) experience with tensor networks, variational Monte-Carlo, machine learning
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. Sequential decision making. MDP/POMDP, dynamic programming, model-based RL, latent state-space (world) models, Monte-Carlo Tree Search. Strong command of at least one, working knowledge of a second: Formal
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, sustainability objectives and future uncertainties into a unified decision-making framework. The candidate will develop probabilistic modelling and stress-testing approaches, including Monte Carlo simulations and
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Monte-Carlo simulations to optimize the detector geometry, as well as in its testing and commissioning. It will include as well the preparation of forthcoming experiments at the DESIR facility in GANIL
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2 PhD Positions in Computational Modelling of Biological Membranes and Adaptive Functional Materials
Monte Carlo simulations together with concepts from statistical physics and non-equilibrium dynamics, you will investigate charge transport, stochastic switching, memory effects and the emergence
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between theoretical and computational high-energy physics. The research contributes to the world-leading PYTHIA Monte Carlo Event Generator, which serves as the baseline for the majority of experimental
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, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
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interferometry, etc.), (v) Predictive modelling of coupled phenomena (reactive transport, rock-water interactions, etc.), (vi) Uncertainty quantification (Monte Carlo, meta-modelling), and (vii) Risk analysis. The
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, machine-learned interatomic potentials, molecular dynamics, kinetic Monte Carlo modelling and comparison with experimental data from the Faraday Institution FAST programme. Faraday Institution PhD students