Discovering and exploiting abstract symmetry elements obtained from low-level data within the CMS experiment


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 explores the development of Physics-Aware Neural Networks (PANN) which require sophisticated data-efficient neural networks that can learn hidden underlying symmetries and generalise from a small dataset.


Total project length: 175/350 hours.

Task ideas

Expected results

Difficulty level



C++, Python, PyTorch, Tensorflow and some previous experience in Deep Learning.


Please use this link to access the test for this project.


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Participating Organizations