Increasing 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. 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". 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. 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.
2. 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, to account for process statistics, process correlations and model uncertainty in the estimation procedure.
For both projects, we are looking for PhD candidates with proven critical thinking skills. Besides an inquisitive mindset, relevant experience in mechanics, numerical methods or machine learning is highly beneficial.
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 your results at international scientific conferences and publish them in academic journals. Furthermore, you will be encouraged to tutor MSc students who do their final assignment on sub-projects pertaining to your research project.
Information and application
Please submit your application by using the “Apply now” button, the application deadline is 31 May 2023, and include:
- curriculum vitae
- letter of motivation
- grades of the BSc and MSc courses
- IELTS or TOEFL score
- contact information of 2 references
The intended starting date is between July and September 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: email@example.com or Dr. Jos Havinga, phone: +31 (0)53 489 6869, e-mail: firstname.lastname@example.org.
First (online) interviews will be held on June 9th, 2023.
A Game-Based assessment will be part of the selection procedure.
About the organisation
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.