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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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mathematical modeling to simulate water fluxes and biogeochemical processes related to carbon and nitrogen cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset
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