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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining Bayesian computational statistics, differentiable programming, and high-performance computing, the...
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multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine learning) to combine datasets and preserve extremes. Uncertainty will be quantified explicitly, with outputs
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currently issues Pollution Risk Forecasts based on statistical models, however, these models often fail to capture key pollution sources such as combined sewer overflows, leading to frequent inaccuracies and
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transforming the theory into scalable production systems, backed by patented innovations. (3) A proven research record in mathematical AI (e.g., optimization, dynamical systems, graph theory, probabilistic