Supervisor: Robert Schöfbeck
Projekt: CMS Analyse
The enormous amounts of data currently accumulated by the CMS experiment hold the key for future precision measurements in high energy physics and, maybe, for discoveries of physics beyond the standard model.
After eight years of operation, conventional strategies to identify, for example, electrons or muons in the vast background of hadronic activity are at their limits. This includes shallow neural nets or boosted decision trees. Recently, however, progress was made with deep neural nets with long short-term memory (LSTM).
I am looking for master students interested in gaining experience in this quickly evolving field and who want to learn how novel machine learning applications cope with a flood of data.
Experience in python and/or C/C++ is beneficial.