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 for estimating particle properties and CMSSW inference engine for use in reconstruction algorithms in offline and high-level trigger systems of the CMS experiment.
C++, Python, PyTorch and some previous experience in Machine Learning.
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