Are you an engineer who want to work on sustainability and recycling of material use? We are looking for a talented and enthusiastic PhD candidate to work on a challenging experimental-numerical micro-mechanics project, in an exciting multidisciplinary team.
Position
PhD-student
Irène Curie Fellowship
No
Department(s)
Mechanical Engineering
FTE
1,0
Date off
29/01/2023
Reference number
V35.6170
Job descriptionThe overarching project: ‘Data Enhanced Physical models to reduce MATerials use' (DEPMAT)
Society calls for an increased recycling of material in steel production processes to reduce the huge CO2 footprint. Current physics-based material models relating composition and thermo-mechanical history are too simple or too computationally expensive for use in industry. DEPMAT aims to develop physics-informed, data-based (machine learning and artificial intelligence) methods for superior accuracy and speed, to enable predictive modelling in industry and to increase recyclability in steel making. We are building a team of talented, enthusiastic researchers to achieve this exciting goal.
PhD vacancy with a focus on in-situ micro-mechanical testing and micromechanical modeling
The goal of this experimental-numerical PhD project is to set up guidelines for providing optimal experimental input for constructing the microstructural part of a physics-informed machine learning approach, with the aim to maximize mechanical accuracy and prediction robustness in light of recycling-induced compositional variations. How to build microstructural models with a minimal level of microstructural detail that finds the optimum between accuracy and computational cost? How to validate accuracy and associated statistical spread of such models, e.g., as function of recycling-induced compositional variations? To this end, you will carry out in-depth microstructural characterization and state-of-the-art in-situ SEM micro-mechanical tests on a few, carefully selected steel grades, to deeply understand their micromechanical behavior. Then you will analyze the mechanical predictions of the physics-informed machine learning approach when trained on different parts of the experimental data, to shed light on the questions above.
Section Mechanics of Materials and the Multiscale Mechanics Laboratory
You will work in the Section of Mechanics of Materials (www. tue.nl/mechmat), Department of Mechanical Engineering, which is globally recognized for its research on experimental analysis, theoretical understanding and predictive modelling of complex mechanical behavior in engineering materials at different length scales (e.g, plasticity, damage, fracture,…), which emerges from the physics and mechanics of the underlying multi-phase microstructure. An integrated numerical-experimental approach is generally adopted for this goal.
You will carry out the state-of-the-art high-resolution in-situ SEM micro- mechanical experiments at the Multiscale Mechanics Laboratory (www. tue.nl/multiscale-lab), led by dr. Johan Hoefnagels (www. tue.nl/hoefnagels-group), which bridges the gap between traditional materials science and mechanical characterization labs, by integrating micro- mechanical testing with real-time and in-situ microscopic observation.
You will closely interact with a numerical PhD student, who aims to establish a multiscale data-driven solution procedure exploiting constitutive equations. Part of your work is a statistical confrontation of simulation results against experimental data.
Job requirementsA meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network with the possibility to present your work at international conferences. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
About us
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands- on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
More information
Do you recognize yourself in this profile and would you like to know more? Please feel free to contact us: dr. Johan Hoefnagels (j.p.m.hoefnagelsattue.nl; www. tue.nl/hoefnagels-group), prof. Marc Geers (m.g.d.geersattue.nl), and dr. Ron Peerlings (r.h.j.peerlingsattue.nl).
Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.Geminiattue.nl.
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Application
We invite you to submit a complete application by using the apply button. The application should include a documents (in PDF format) :
We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.