Image Processing of Transmission Electron Micrographs

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]