PhD; Bayesian uncertainty quantification for flow simulations of soft materials

The Eindhoven University of Technology (TU/e), Department of Mechanical Engineering has a vacancy for a PhD Student on Bayesian uncertainty quantification for flow simulations of soft materials in the Polymer Technology group.

Department(s)

Mechanical Engineering

Reference number

V35.4786

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.

More information

Do you recognize yourself in this profile and would you like to know more? For more information about the project, please contact dr. N.O. Jaensson, n.o.jaensson[at]tue.nl or dr. C.V. Verhoosel, c.v.verhoosel[at]tue.nl.

For information about terms of employment, click here  or contact HRServices.gemini[at]tue.nl.

Please visit www.tue.nl/jobs to find out more about working at TU/e!

Application

We invite you to submit a complete application by using the 'apply now'-button on this page.
The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Detailed curriculum vitae, including a list of your publications and the contact information of three references.
• Grade list for your BSc and MSc grades and a brief summary of your MSc thesis.
• List of references with full contact information (including the supervisors of your final BSc and final MSc projects).
• At least one recommendation letter.

We look forward to your application and will screen it as soon as we have received it. Only complete applications will be considered. We do not respond to applications that are sent to us in a different way.

Review of applications will begin immediately and continue until the position is filled. Promising candidates will be contacted by email. Starting date of the position is as soon as possible.

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