This is NTNU
NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim.
At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world.
You will find more information about working at NTNU and the application process here.
About the job
We have a vacancy for an enthusiastic and modelling-inclined PhD candidate to work in application of machine learning methods within the field of crystal and/or continuum plasticity. The position belongs to the Physical Metallurgy group (https://www.ntnu.edu/ima/research/physical-metallurgy#/view/about), in the Department of Materials Science and Engineering, Norwegian University for Science and Technology (NTNU).
This academic position will provide the opportunity for the candidate to complete a doctoral education program and obtain a PhD from NTNU, while undertaking professional development activities within the broad fields of research and materials science.
Crystal plasticity plays a critical role for understanding and predicting both the global and local behavior of materials under various loading conditions, especially in metals and alloys. Phenomena such as a corner effect, non-normality of the plastic flow, flow localization, are particularly suited for being studied within the crystal plasticity framework. In a hierarchical multiscale modelling paradigm, the crystal plasticity, as a computational method, stretches itself broadly along the microscale, neighbouring from the bottom with the dislocation and molecular dynamics, and is very closely linked to the upper scale of the continuum plasticity. Here, plastic anisotropy evolution associated with the texture evolution are essential for improving forming simulations. Given the recent improvements, both in computational power and in the effectiveness of the numerical methods, it increasingly becomes viable to simulate the large plastic deformations of small and medium sized components, made of single or multi-materials, directly by using the crystal plasticity methods.
Machine learning techniques, especially recurrent neural networks, are among the current research activities, used for representing the constitutive equations and calibration of advanced anisotropic continuum plasticity models based on data generated by the crystal plasticity. This project aims at making an advancement within the field of crystal and continuum plasticity strongly supported by the relevant machine learning methods.
You will report to Associate Professor Tomáš Mánik. The PhD position is funded by the Department of Materials Science and Engineering.