PhD; Bayesian uncertainty quantification for flow simulations of soft materials

We are looking for a PhD student for a four-year research project on the topic of uncertainty quantification for flow simulations of soft materials. In this computational/theoretical project you will develop and perform finite element simulations, which will be analysed using the Bayesian statistical framework.

Job description

Soft materials, a.k.a. complex fluids, are materials with a microstructure that significantly influences its response to external loading, i.e. their 'constitutive behavior'. Examples are polymeric liquids, foams/emulsions and particle suspensions, and they are encountered all around us in everyday life. Moreover, soft materials are present in many industrial applications, e.g. in the fields of foods, pharmaceuticals, robotics and energy. For the rational design and optimization of processes involving these materials, flow simulations are of the utmost importance. However, accurately calibrating constitutive models for new materials is a daunting task, and it is often difficult to know a-priori how these models perform in complex flows. The aim of this project is an uncertainty quantification in flow simulations of soft materials using the Bayesian statistical framework. High-fidelity FEM simulations will be developed to solve the deterministic problem of the flow of materials with a given constitutive behavior. The simulations will then be used, in combination with simple experiments, in the Bayesian analysis for uncertainty quantificaiton. As part of this project, we will explore the use of surrogate models using physics-informed machine learning, which will enable the Bayesian UQ for more complex simulations. The emphasis of the project is on the FEM computations and the Bayesian framework, but a small experimental part might be possible, depending on the expertise and interest of the candidate. The PhD position is in the Polymer Technology group, in collaboration with the Energy Technology group.