Machine Learning
We deploy machine learning algorithms to optimise data analyses in electron microscopy. Ongoing projects include:
- A model-independent zero loss peak (ZLP) subtraction in electron energy-loss spectroscopy (EELS) with neural networks;
- Automatising the classification and identification of relevant features in Hyperspectral EELS images;
- Deep learning classification of crystalline structures and defects from atomic resolution TEM images.
Meet the developers team:
- Laurien Roest (Former member, TUD)
- Isabel Postmes (Former member, TUD)
- Juan Rojo (Assoc. Prof., Nikhef)
- Jaco ter Hoeve (PhD candidate, Nikhef)
- Abel Brokkelkamp (PhD candidate,TUD)
- Luigi Maduro (PhD candidate,TUD)
- Helena La (PhD candidate, TUD)
- Sonia Conesa-Boj (Assoc. Prof., TUD)