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 project

• A MSc. degree in Computational mechanics, Computational materials science, Mechanical engineering, Applied physics, Data science or a related field with excellent grades.
• Special interest in modelling of production processes.
• A background in nonlinear solid mechanics, computational methods, material science and/or data science.
• Strong programming skills.
• A high degree of responsibility and independence.
• Strong communication skills for effective academic and industrial collaboration.
• Proficiency in English is required, both spoken and written (IELTS minimum score 6.5 or TOEFL-iBT minimum score 90).

• A dynamic and international environment, combining the benefits of academic research with a topic of high industrial relevance;
• excellent working conditions in an exciting scientific environment, and a green and lively campus;
• a fulltime 4 year PhD position;
• excellent mentorship and facilities;
• a professional and personal development program within Graduate School Twente;
• a starting salary of € 2.541 in the first year and a salary of € 3.247 in the fourth year gross per month;
• a holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%;
• minimum of 29 holidays per year in case of fulltime employment;
• full status as an employee at the UT, including pension and health care benefits.