In the coming upgrade to the Trigger system of the CMS Experiment many machine learning algorithms are being developed which will use low-level detector information to make decisions on whether to keep data or not. These algorithms will be exposed to a dynamic inference environment that will not only differ from their training environments but evolve over time as the detector conditions change. In the context of the Next Generation Trigger project you will explore continual learning approaches to incrementally updating trigger algorithms with new data specifically focusing on decision tree-based algorithms. The project will explore changing environments in the CMS detector as well as designing novel ways of training and updating decision trees. There will be opportunity to explore the implementation of these continual learning training techniques within the wider CMS trigger ML training infrastructure.

Your profile

• You are currently studying in Applied Physics, Engineering Physics or a related field.
• You are required or encouraged by your university to complete a practical training period.
• You are eager to develop your skills through on-the-job learning.
• You enjoy teamwork and learning from others in a collaborative setting.

Be sure to meet the eligibility criteria

• You have completed at least 18 months of your studies (3 semesters) in the respective field at the time of the selection. PhD students or holders are not eligible.
• You have a good knowledge of English or French.
• You remain enrolled as a full-time student during your studentship.