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 graph neural networks for particle (tau) identification and CMSSW inference engine for use in reconstruction algorithms in offline and high-level trigger systems of the CMS experiment.
Total project length: 175 hours.
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