Opportunity to welcome a foreign student (= not in France for 12 months over the last 3 years) to work on uncertainties induced by ML models in the context of new physics searches at the LHC

Precise estimation of uncertainties is a crucial asset in the search for new physics at the
LHC. While neural network based simulation and analysis methods have enable a more
efficient treatment of high-dimensional data, a rigorous treatment of network induced
uncertainties remains elusive.
In the research project the PhD student will explore different methods to estimate network
induced uncertainties. Starting from toy examples that highlight limitations of interpolation
and extrapolation the student will analyse the properties of multiple methods including
Bayesian Neural Networks, ensemble methods and mutual information. Once advantages
and limitations of each method are understood, the student will apply them to complex high
energy physics problems like jet unfolding and event simulation