We are pleased to announce the "Chemical and materials machine learning school" (CAMMLs) that will run 5th - 9th April 2027 at STFC - Daresbury Laboratory, United Kingdom; this international machine learning for materials training course is being run in collaboration between PSDI, and AIchemy, with support from STFC-SCD, CCP5, CCP9 and CECAM. This training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field, who have experience of coding but are not highly experienced with machine learning. The aim of this training is to introduce attendees to the latest methods of machine learning for the atomistic simulation of materials and molecules. A limited number of places may be available for industrial applicants. https://spring2027.camml.ac.uk This training will encompass a number of talks and practical sessions, focusing on the basics of machine learning, machine learning interatomic potentials and graph neural networks. There will also be the opportunity for attendees to present a poster on their work. Learning outcomes Awareness of the state-of-the-art methods for machine learning for atomistic and molecular simulations Hands on experience of using machine learning for atomistic and molecular simulations Applications are now open for this training course. The application deadline is 26th of November 2026. You will be informed of the outcome of your application by 17th December 2026, and payment is required by 13th February 2027. The fee for this course will be £250 per student, this will include 5 nights accommodation, breakfast, lunches, coffee breaks, school bbq and poster session refreshments. Please note: places on this course are limited to 40 and in the event of oversubscription to the training course we will favour a diverse group of attendees.