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We are hosting a monthly meeting open to students and anyone interested in machine learning for accelerators and related fields. The aim is to share progress on projects, troubleshoot common issues (coding errors, file organization, data standards, repositories), and exchange practical know-how. We’ll also use the space to keep each other updated on conferences, abstract submissions and deadlines, new papers, and relevant news.
This is an informal but structured gathering: a place to discuss what you worked on last month, learn from each other’s experiences, and make sure nobody gets stuck in isolation. Whether you’re working on experiments, simulations, or coding pipelines, this is a chance to get feedback, stay on top of developments, and build good habits as a community.