PhD: Statistical Interpretation of microstructures for improved predictive modelling – Technical University of Delft, Delft (the Netherlands)

////PhD: Statistical Interpretation of microstructures for improved predictive modelling – Technical University of Delft, Delft (the Netherlands)


For Through Process Modelling, the study of microstructural properties in steels is crucial. Microstructural quantities of interest include grain size, particle size, grain shape, grain orientation and grain mis-orientations, spatial distributions and anisotropy. Using in-house characterization techniques at TATA steel, profiles of 2D-sections of 3D samples can be obtained. Extracting information on the 3D features based on the 2D pictures, is a stereological problem. An essential step in this process is to describe the formation of microstructures using a realistic well defined stochastic model. Then, the “direct problem” needs to be solved, expressing the stochastic behaviour of the 2D features in the profile in terms of important properties of this stochastic model. Finally, the data need to be used to extract information on the important 3D features. There will be focus on joint behaviour of features as well as (joint) extremal behaviour.


The challenge in this project is to apply statistical methodology on state of the art models from materials science to address stereological problems. Nonparametric estimation, extreme value statistics and stochastic simulation will be important subjects in this project.

At TU Delft, the candidate will join the Statistics section within Delft Institute of Applied Mathematics. The latter is one of the departments within the faculty of Electrical Engineering, Mathematics and Computer Science

The Statistics group covers one of the key research areas at DIAM. It aims at developing theory within the field of mathematical statistics as well as applying state-of-the-art statistical theory to problems from practice. Within DIAM, there is close collaboration with the applied probability group, exemplified by a joint weekly seminar and joint educational efforts (basic probability and statistics courses and courses in the Finance minor and master tracks) and active participation in Delft Data Science (DDS). The Statistics Helpdesk constitutes an interesting interface with other disciplines present at the TU Delft, leading to new interesting statistical problems and appreciation of statistics by other research groups. The group is relatively small and communication lines are short.


This research project is a cooperation between DIAM, the Faculty of Materials Science and Engineering and Tata Steel Europe. Your principal work place will be at DIAM in Delft. But on a regular base you will work at the Lab of Tata Steel in IJmuiden (NL). Here you will meet and work together with researchers from Tata and other PhD researchers. The large multidisciplinary project (with a total of 17 PhD students involved) is on finding relations between process parameters in steel production and mechanical properties of the resulting product.

This project needs knowledge of the various physical processes involved in steel production, knowledge on how to measure relevant parameters and also knowledge on mathematical and statistical modelling of the data. In this project it is your role to bring in the needed mathematical and statistical knowledge, insights and methods for better predictive modelling.

A central role in the project is taken by so-called microstructural quantities of the steel. On one hand these are influenced by the process parameters that can be controlled. On the other hand, these influence the mechanical properties of the product. Microstructural quantities include grain and particle size, grain shape, orientation and mis-orientations, spatial distributions, and anisotropy. Characterisation techniques can obtain profiles of 2D sections of 3D samples. First, the formation of microstructures should be described using a realistic and physically inspired stochastic model. Then, the stochastic behaviour of the 2D visible features will need to be expressed to extract information on the statistics of important 3D features. The focus is on joint behaviour of features as well as (joint) extremal behaviour. The challenge in this project is to use state-of-the-art models from materials science and develop statistical methodology to address the stereological problems. Nonparametric estimation, extreme value statistics, and stochastic simulation will be important subjects.


The candidate possesses an MSc degree in mathematics, physics, computer science (specialized in machine learning) or another relevant area. Furthermore:

  • Strong interest in statistical methodology, physical applications and computational methods.
  • Proficiency in reading scientific papers in English is a must as are writing skills.
  • Ambition, organizational and communication skills (you work in a team in which you are the sole expert on statistics) and highly motivated to achieve tangible results.


The TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

The PhD candidate will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit for more information.


Minds for Innovation is the recruitment partner for this project. For more information regarding recruitment please contact Jolanda de Roo. E:, T: + 316 572 913 50

For more information on the project specifics, you can e-mail Prof. Geurt Jongbloed. E:


Application documents (in PDF format) must contain:

  • letter of motivation containing why you are interested in this specific project and why you would be a good candidate
  • detailed curriculum vitae and contact information of two potential referees
  • transcripts of BSc and MSc grades
  • transcript of English test

Apply before the deadline of July 31st 2018.

As one of the first selection steps, you will be invited to participate on our specialised online assessment. If you would like to know more about why we use this and how we have set it up, then check out our website.

2018-06-20T02:10:10+00:00 May 29th, 2018|