Semi-supervised learning approaches for defect detection in nuclear materials fabrication
This project from the UTSA Vision & Artificial Intelligence Lab in Computer Science is part of a larger collaboration with the UTSA Extreme Environment Materials Lab in Physics, focused on the application of novel AI methods to materials fabrication. The project will explore unsupervised and semi-supervised learning approaches to detecting defects in images of fabricated nuclear materials. Analysis of these materials and defects will help to inform the fabrication process for our collaborators.
- Faculty: Amanda Fernandez
- Department: Computer Science
- Open Positions: 3
- Mode: Hybrid
- Hours per week: 10-15
Requirements and Responsibilities:
Needed Skills: Students should be familiar with python for data science and machine learning, including PyTorch. Familiarity with LaTeX is preferred. The required prerequisite course minimum is CS 3753 Data Science, and applicants with subsequent courses in AI, ML, DL, etc will be preferred.
Student Responsibilities: Undergraduate fellows will be responsible primarily for developing code and running experiments on our data. Students will be expected to read the research papers we select for group discussion and leverage any code available through those papers.
