Most technologically useful materials are polycrystalline microstructures composed of a myriad of small monocrystalline grains separated by grain boundaries. The aim of the project is to develop image processing algorithms and codes to automatically trace grain boundaries in bright-field transmission electron micrographs for subsequent statistical analysis of microstructural metrics, both static and dynamic. The project will use machine learning approaches of correlation neural networks.
Position Dates: Summer 2022
Direct Supervisor: Matthew Patrick
Lab: Barmak Lab
Hours per week: 25 hr/week
Number of positions: 1
Position is paid
Position is not available for credit
Position type: Hybrid (both remote and on site)
Qualifications: Python programming
Eligibility: Freshman, Sophomore, Junior
SEAS students only: No
Prof. Katayun Barmak, [email protected]