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.
This project will focus on the development of end-to-end deep learning regression to estimate the particle properties, such as mass of a particle.
Total project length: 175/350 hours.
C++, Python, PyTorch and some previous experience in Machine Learning.
Please use this link to access the test for this project.
Please DO NOT contact mentors directly by email. Instead, please email email@example.com with Project Title and include your CV and test results. The mentors will then get in touch with you.