X13 - Machine Learning for Electron Microscopy: from Data Analysis to Active Experiments

A major focus of the course will be on engineering the transition from human-controlled to AI-augmented and fully autonomous workflows. Special emphasis will be placed on the AutoScript interface, which allows researchers to implement Python-based automation directly on Thermo Fisher electron microscopes, providing hands-on experience in running real-time data analytics, automated imaging optimization, and AI-driven decision-making workflows. Attendees will learn how to develop and deploy ML workflows on their own microscopes, tackling key challenges such as real-time API integration, stochastic optimization for decision-making, and adaptive AI models. Whether you are interested in microscopy data analysis, automated instrument control, or AI-enhanced materials discovery, this course will provide a comprehensive overview of the state-of-the-art and future directions in the field.

Note: Additional registration rates apply to participate in individual Short Courses.