Growing the proportion of recycled materials (scrap) in steel production is a key step towards reducing its environmental footprint. To maintain high product quality standards for steels with higher scrap content, it is needed to drastically improve the predictive capability and calculation speed of steel processing models, both at microstructural and at macroscopic level. This challenge is pursued by combining powerful physics-based modelling with machine learning techniques, creating new and efficient hybrid models for process design and control.
These PhD positions are part of a large national research project about "Data Enhanced Physical models to reduce Materials use" (https: // depmat.nl/). The projects will be performed in close collaboration with industry and with researchers from other Dutch universities, to increase the impact of the work. Each PhD project will focus on one of the components of the modelling framework, being:
1. Physically consistent data-driven constitutive models by machine learning
This project's goal is to develop highly efficient and predictive material models that can be used in simulation of forming processes. The key aspect of these models will be to incorporate physics-based information relating to microstructural features and fluctuations in the composition of the material. Machine learning approaches will be investigated to build a model that will be trained using data from crystal plasticity simulations and will be thermodynamically consistent thanks to a physics-based architecture.
2. Inline hybrid modelling in cold rolling and forming
The objective of this PhD project is to develop highly accurate hybrid models that can be used to relate indirect process measurements in metal forming processes (e.g. process forces or intermediate product geometry) to the material, product, and process properties. Key challenges in this respect are the limited accuracy of physics-based models, incomplete production data, uncertain fluctuations in process conditions and requirements for fast models. A new type of process model must be developed, by exploiting the strength of physics-based simulation models and of real-time production data.
3. Inline probabilistic state estimation and model correction
In this PhD project, fast and accurate procedures will be developed to simultaneously estimate process conditions and apply hybrid model correction. The developed procedures must be applicable in real-time during production. The methods must be formulated within a probabilistic framework and will therefore require a specific focus on the estimation of process statistics, process correlations and model uncertainty.
For these three projects, we are looking for PhD candidates with relevant expertise. You will report your research during bi-weekly meetings of our research group and frequent meetings with industrial and academic partners. You are encouraged to interact significantly with the project partners and present their results at international scientific conferences and publish them in academic journals. Furthermore, as researcher you will be encouraged to tutor MSc students who do their final assignment on sub-projects pertaining to the current research projectYour profile
Please submit your application before January 13th, 2023 using the “Apply now” button, and include:
The intended starting date is between March and June 2023.
For more information you can contact: Prof. Ton van den Boogaard, head of the chair Nonlinear Solid Mechanics, phone: +31 (0)53 489 4785, e-mail: firstname.lastname@example.org,
Dr. Semih Perdahcioglu ( position 1 ), phone: +31 (0)53 489 2675, e-mail: email@example.com, Dr. Jos Havinga ( positions 2 and 3 ), phone: +31 (0)53 489 6869, e-mail: firstname.lastname@example.org.
First (online) interviews will be held on January 24th and 27th, 2023.
A Game-Based assessment will be part of the selection procedure.About the organization
The Faculty of Engineering Technology (ET) engages in education and research of Mechanical Engineering, Civil Engineering and Industrial Design Engineering. We enable society and industry to innovate and create value using efficient, solid and sustainable technology. We are part of a ‘people-first' university of technology, taking our place as an internationally leading center for smart production, processes and devices in five domains: Health Technology, Maintenance, Smart Regions, Smart Industry and Sustainable Resources. Our faculty is home to about 2,900 Bachelor's and Master's students, 550 employees and 150 PhD candidates. Our educational and research programmes are closely connected with UT research institutes Mesa+ Institute, TechMed Center and Digital Society Institute.How to apply Step 1
Apply. When you see a vacancy that appeals to you, you can apply online. We ask you to upload a CV and motivation letter and/or list of publications. You will receive a confirmation of receipt by e-mail.Step 2
Selection. The selection committee will review your application and you will receive a response within 2 weeks after the vacancy has been closed.Step 3
1st interview. The 1st (online or in person) meeting serves as an introduction where we introduce ourselves to you and you to us. You may be asked to give a short presentation. This will be further explained in the invitation.Step 4
2nd interview. In the second interview, we will further discuss the job content, your skills and your talents.Step 5
The offer. If the conversations are positive, you will be made a suitable offer.Want to know more Boogaard, A.H. van den (Ton)
Vice-dean Research, Faculty Engineering Technology; Professor Nonlinear Solid MechanicsBoogaard, A.H. van den (Ton)
Vice-dean Research, Faculty Engineering Technology; Professor Nonlinear Solid Mechanics
Do you have questions about this vacancy? Then you can contact Ton for all substantive questions about this position and the application procedure. For general questions about working for the UT, please refer to the chatbot.Contact
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