Our Laboratory Experimental Continuum Mechanics is offering a
The research project aims to use cellular automata (CA) approach for prediction of the microstructure and texture of selectively laser melted gas turbine blades made of a Ni-base superalloy. Surrogate modelling (machine learning) will be employed for moderating the computational cost of CA for the component-scale simulations. The outcomes of the microstructural simulations will be exploited by an FE-based crystal plasticity model for assessing the high-temperature creep-fatigue deformation and failure of the turbine blade.
A set of laboratory test pieces will be printed and characterised in term of microstructure, texture and mechanical response to generate input data for calibration of the CA and crystal-plasticity models. The verification of the modelling approach will be based on observations from microstructure and mechanical response of the printed turbine blade.
We are looking for a highly motivated team player ready to work independently. The ideal candidate possesses an MSc or PhD degree in materials science, mechanical engineering or related fields. The candidate should have a sound basis in numerical modelling. Prior experience in microstructure modelling, crystal plasticity and machine learning is an advantage. Excellent communication skills and good verbal and written knowledge in English are a prerequisite. Knowledge of German is considered advantageous. The position is available upon agreement.