Ongeveer 21 uur geleden - Radboud Universiteit Nijmegen (RUN) - Nijmegen
As a PhD candidate you will be responsible for research activities within the NORFACE/Belmont forum T2S programme ‘Securing Tenure, Sustainable Peace?', a …
Nonparametric estimation, extreme value statistics, and stochastic simulation
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 (specialised in machine learning), or another relevant area. Additional qualifications:
• Strong interest in statistical methodology, physical applications, and computational methods.
• Proficiency in reading and writing scientific papers in English.
• Ambition, motivation, and good communication skills (high level of English proficiency).
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.
As a PhD candidate you 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 graduateschool.tudelft.nl/ for more information.
Minds for Innovation is the recruitment partner for this project. For more information regarding recruitment please contact Jolanda de Roo. E: firstname.lastname@example.org, T: + 316 572 913 50
For more information on the project specifics, you can e-mail Prof. Geurt Jongbloed. E: G.Jongbloed@tudelft.nl
To apply, please use the online application by July 31th 2018:
When applying for this position, please refer to vacancy number EWI2018-01 in the subject of your motivation letter.
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