PhD on ML accelerated simulations and uncertainty quantification of composites

Introduction

We are hiring! We have an open position for a PhD candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning.

Job Description

• The context: Natural fiber reinforced materials are increasingly used as environmentally friendly alternatives for composite materials, due to the sustainability and circularity of the natural fibers (e.g. hemp, flex, bamboo). Natural fiber reinforced composites have applications in different engineering fields, including construction (used for insulation and load bearing members), automotive (used for interior components), and consumer products (e.g. in sporting products). The natural fibers used in these composites are sensitive to moisture-induced and chemical degradation. Moreover, they exhibit a high degree of variability in their composition and microstructure, which results in variability in their mechanical, thermal, and diffusion properties. Therefore, predictive modeling of the performance of these composites is a multidisciplinary problem centered around computational mechanics and engineering, potentially involving (i) multiphysics problems, (ii) multiscale methods, (iii) uncertainty quantification and (iv) machine learning.
• The project will focus on one or a combination of the following focus areas, tailored to the candidate’s expertise and interests: 
  • Developing numerical models for the coupled mechanical-diffusive(-thermal-chemical) behavior of fiber-reinforced composites at the micro/meso-scale. These models will incorporate uncertainty via stochastic inputs, such as random field representations of spatially varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations.
  • Identifying the variability of the model parameters using Bayesian inference.
  • Quantifying the impact of microscale uncertainties on macroscale material performance through stochastic homogenization and uncertainty propagation methods, including Monte Carlo and Gaussian Process-based approaches.
  • o Integrating machine learning techniques to replace computationally expensive simulations and enable faster predictions while preserving uncertainty information.
• The successful candidate will work in the chair of Applied Mechanics, Department of the Built Environment, under the supervision of dr. Emanuela Bosco and dr. Payam Poorsolhjouy, as well as dr. Lars Beex (University of Luxembourg) . The chair of Applied Mechanics is responsible for education and research in the field of mechanics, working on multiscale, multi-physics and optimization problems related to the built environment. The chair is a member of the Graduate School of Engineering Mechanics, Netherlands. This graduate school offers PhD students an advanced training program in engineering mechanics, the core of which is a joint series of advanced graduate courses closely connected to state-of-the-art research themes.
• The successful candidate will interact closely with other PhD students (numerical and experimental) who work on other aspects of the mechanical and multiphysics behavior of heterogeneous materials. 

Job Requirements

•  A talented, motivated and enthusiastic researcher. Analytical skills, initiative and creativity are highly desired.
• A MSc-degree in Mechanical Engineering, Civil Engineering, Computational Mechanics, Mathematical Engineering or equivalent is required.
• A strong background in mechanics of materials and multi-scale and multi-physics methods is highly desired.
• Additional expertise in uncertainty quantification and/or machine learning techniques is advantageous.
• Interest to work in an interdisciplinary project that incorporates different fields of expertise, including analytical/computational frameworks, uncertainty quantification, and machine learnig to solve problems with significant social and industrial impact is beneficial.
• Excellent oral and writing skills in English are required (C1 level).

Conditions of Employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you: 

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment. 
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 2,901 max. € 3,707).  
• A year-end bonus of 8.3% and annual vacation pay of 8%. 
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process. 
• An excellent technical infrastructure, on-campus children's day care and sports facilities.  
• An allowance for commuting, working from home and internet costs. 
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager dr. Emanuela Bosco, e.bosco@tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact hrservices.be@tue.nl...

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.
• Brief description of your MSc thesis.
• Transcripts of BSc and MSc programs including courses attempted and grades achieved.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.