Machine learning for phenomenological applications

Machine Learning technique have seen a massive rise in popularity and their use has permeated a very wide range of applications in many scientific, commercial and societal fields. The rapid development of new techniques, algorithms, software and dedicated hardware has created a multitude of new opportunities. While Machine learning has played a crucial role initially in the analysis of particle physics data, more recently, ML algorithms have found a multitude of applications in more theoretical aspects of particle physics.

The PhD project will involve the development of reliable emulators for complicated higher order calculations, applications of ML algorithms to Monte Carlo integration optimization or the application of modern density estimation techniques to particle physics cross sections.