Assistant/Associate Professor in Health Technology Assessment / Health Economics of Digital Innovations
Ongeveer 17 uur geleden - Universiteit Twente (UT) - Enschede
Understanding dislocation structure
This PhD project forms part of the Digitally Enhanced New Steel Product Development (DENS) program, in which Tata Steel Europe, Materials innovation institute (M2i) and several academic partners collaborate to enable the development of new generations of advanced materials for e.g. the automotive industry.
Digitally Enhanced New Steel Product Development (DENS) program
Significant progress has been made in the past decades in the development of advanced models that describe the behavior of steel during processing and subsequent applications. However, the quantitative application of through process models in new steel product development still lacks predictivity. In modern steel grades, parameters of the steel production process have a significant influence on the final material properties. Furthermore, the trend towards complex multi-phase microstructures requires very sophisticated models to describe their mechanical properties in a predictive manner. The main scientific challenge addressed in this program is to integrate state-of-the-art models in a single through process model framework that can be applied in practice for new steel product development.
Plastic deformation of metals causes a significant increase of the dislocation density, and thus of the defect energy in the material. During subsequent annealing, softening takes place by three processes: recovery, recrystallisation and grain growth. All three can also occur dynamically, i.e. during hot deformation. The aim of the project is to develop physical understanding of the development of the dislocation structure during recovery, to understand nucleation in recrystallisation in relation to the dislocation structure and to determine the influence of chemical composition on grain growth. Experimental data will have to be acquired to test and validate existing models thoroughly, and where needed will be extended. Systematic measurements on the dislocation segment length will give insight in the quantification of the dislocation structure on a single sample during repeatedly interrupted annealing treatments. Based on this understanding, the softening processes in steel on the microstructural scale will be described in physically based models. The essentials of the physical models are to be captured in a simplified on-line model for application during the production process.
We are searching for enthusiastic candidates holding an MSc degree in physics, chemistry, materials science, or a similar field. The candidate must have knowledge of physics of materials, solid state physics, solid-solid phase transformations, and mechanical behaviour. Moreover, the candidate should have:
• affinity for computational calculation as well as a solid background in mathematics and numerical methods.
• an “academic attitude” with excellent analytical skills, initiative, inventiveness, curiosity, and critical thinking about the research approach and results.
• strong communication skills (high degree of English proficiency), ability to integrate, and good social behaviour.
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.
For more information about this position, please contact Professor J. (Jilt) Sietsma, e-mail: firstname.lastname@example.org.
Minds for Innovation is the recruitment partner for this project. For more information regarding recruitment please contact Jolanda de Roo. E: email@example.com, T: + 316 572 913 50.
To apply, please visit the website www.m4i.nl/vacancies/ and use M4i's online application tool by 31 July 2018. When applying for this position, please refer to vacancy number 3ME17-78 in the subject of your motivation letter.
Jolanda de Roo
Technische Universiteit Delft