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.

• We are looking for an experienced candidate with a MSc degree (or about to obtain one soon) in mechanical engineering, applied physics, applied mathematics or similar.
• The candidate should have knowledge of continuum mechanics and numerical methods (such as the finite element method) combined with strong programming and mathematical skills and a good physical intuition.
• Experience with Bayesian uncertainty quantification or machine learning are a plus.
• The ideal candidate has excellent scientific skills as well as excellent soft skills related to verbal and written communication (in English).

We offer

• A challenging full-time employment for four years, with an intermediate evaluation after one year, in a highly motivated team at a dynamic and ambitious University. You will be part of a highly profiled multidisciplinary collaboration where expertise of a variety of disciplines comes together. The TU/e is located in one of the smartest regions of the world and part of the European technology hotspot 'Brainport Eindhoven'; well-known because of many high-tech industries and start-ups. A place to be for talented scientists!
• To support you during your PhD and to prepare you for the rest of your career, you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.