In a more sustainable world recycling is essential.  Recycling of steel causes high concentrations of impurity elements such as copper, that segregate at grain boundaries.  Such local segregation has a big impact on physical phenomena during processing and on the final properties of products. There are many types of grain boundaries and many types of impurities, while modeling physical phenomena can be challenging in its own right.  Therefore significant computational resources are required for predictive computational modeling.  To optimally utilize modelling data, and to enhance predictive ability, machine learning in various forms is employed.

The project presents a unique opportunity to work in a collaborative team and consortium of academic research groups and industrial partners. This position will be embedded in the research groups of Dr. Marcel Sluiter (TU Delft & U Ghent), Dr. Sid Kumar (TU Delft), and Dr. Kees Bos (Tata Steel Europe and TU Delft) along with collaboration with Dr. Miguel Bessa (Brown University).

Apply through https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?jobId=9688