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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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, 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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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference
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. These methods must be embedded in an iterative procedure to compute the final design and are based on Monte-Carlo simulation. They are known to have slow convergence. Using the Hamiltonian structure of the system