One of the important aspects of searches for new physics at the Large Hadron Collider (LHC) involves the identification and reconstruction of single particles, jets and event topologies of interest in collision events. The End-to-End Deep Learning (E2E) project in the CMS experiment focuses on the development of these reconstruction and identification tasks with innovative deep learning approaches.
Total project length: 175/350 hours.
Please DO NOT contact mentors directly by email. Instead, please email firstname.lastname@example.org with Project Title and include your CV and test results. The mentors will then get in touch with you.