Uncertainty Quantification in Dynamic Biological Systems

Uncertainty can enter mathematical models and experimental measurements in various contexts. The study of the behaviour of parameterized non-linear dynamic models is often impeded by lack of knowledge of a subset of parameters (uncertainty) and/or non-uniqueness (variability) of another subset of the parameters. A core topic in the field of uncertainty quantification is the question how uncertainties in model inputs are propagated to uncertainties in model outputs. How can one characterize the distribution of model outputs, given the distribution of the model inputs? Clearly, straightforward Monte Carlo sampling can be used, but in practice, considerations of efficiency and computational cost often preclude this approach. Among others uncertainty quantification methods such as stochastic collocation, polynomial chaos expansion, generalized polynomial chaos and stochastic Galerkin methods have been employed.

This Ph.D. position on Uncertainty Quantification is in the context of Predictive Food Modelling or Predictive Food Microbiology. Gene expression is a fundamentally noisy process, giving rise to a significant cell-to-cell variability at the phenotype level. The phenotypic noise is manifested in a wide range of microbial traits. Heterogeneous behaviour of individual cells is observed at the growth, survival and inactivation responses and should be taken into account in the context of Predictive Food Microbiology. The Ph.D. project will be embedded in a larger project in which we aim to build a transparency cloud solution for the food supply chain that gathers the detailed data on each product unit on its way to the consumer and to use this data to improve the current food safety processes.

This is a computational PhD project with a strong focus on computer programming and mathematics. If you do not have a solid background in these topics, please do not apply.