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of radionuclides on clay mineral surfaces using DFT Kinetic Monte Carlo simulations with activation energy barriers as input to simulate large-scale interactions of nuclides with surfaces Preparation and
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quantitatively experimental data in different polymeric systems with different self-poisoning mechanisms, that classical theories were unable to handle. Monte Carlo and Molecular Dynamics simulations will also be
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longitudinal data modelling, High-dimensional data analysis A3 Strong knowledge of computational tools including: Differential equation-based modelling (ODE/PDE tumour models), Bayesian or Monte Carlo inference
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for structural load-bearing capacity, deterioration processes, or life-cycle-based analysis of structures - Experience with Monte Carlo simulation, FORM/SORM, or similar methods - Ability to work with experimental
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, electroweak and Higgs physics, flavour physics, model building, Monte Carlo simulations, precision standard model physics, neutrino physics, particle astrophysics, strong interactions and novel computational
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, stochastic dynamics, Monte Carlo and analytical methods) and its thorough validation using advanced experimental techniques (such as mass spectrometry, electron microscopy, radiochemistry and radiobiology
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triggered by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate
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, using a combination of well-established and robust analysis tools (e.g. mu-analysis) and Monte-Carlo time domain non-linear simulations, including variations in all the uncertainties expected in
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by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion
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-Experience in particle physics phenomenology or related area - programming in C++ - programming in Python - fluent knowleage of English language Welcome: - experience in Monte Carlo methods and statistical