The PhD supervisors and projects for studentship beginning in October 2022 are listed below. Applications for these IPPP studentships are to be submitted through the Centre for Particle Theory (CPT) – See here for further information and instructions on how to apply.

Searching for New Physics Phenomena beyond and within the Standard Model

In the last decades, the Standard Model of particle physics evolved to the most precise theory of fundamental interactions and the elementary constituents of matter. Despite its great success, there remain open questions: the Standard Model cannot account for the dark matter content of the universe, it does not explain why the Higgs boson mass is so much smaller than the Planck mass or why QCD does not break CP-symmetry. It also does not explain the observed matter-antimatter asymmetry of the universe, which must have been present shortly after the Big Bang, and this remains one of the major outstanding questions in modern physics.

And even within the Standard Model itself, there exist fascinating quantum and non-perturbative phenomena that are predicted by Quantum Field Theory, but have never been observed in any particle physics experiments. These are related to semiclassical field configurations such as instantons which describe quantum tunnelling effects, monopoles, skyrmions and other soliton-like configurations.

This project will develop theoretical and phenomenological approaches to search for fundamental new physics phenomena in the Standard Model itself and in Beyond-the-Standard-Model formulations to address these exciting issues.

Machine learning for phenomenological applications

Machine Learning techniques have seen a massive rise in popularity and their use has permeated a 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 (ML) 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.

Possible PhD project include 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.

Science Graph

Predictions for Collider Processes at High Energy

The Standard Model of particle physics, and its possible extensions, are tested by comparing data from LHC experiments with precise predictions. The imminent increase in the rate of data taking at the LHC experiments will allow for the study of new processes and the study of high energy regions of phase space, where the limited data collected so far has not allowed for precise comparisons.

Some of these searches will relate to regions of phase space, where the calculated perturbative coefficients of the theory predictions have systematically large contributions, which need to considered to ensure a stable prediction. A stable prediction is of course necessary to decide whether any possible deviation is because of fundamentally new physics, or because of a lack of understanding of known physics.

The project will further develop a framework for precise predictions taking into account the systematic high energy corrections. Current projects involve both the analytic calculation of logarithmic corrections, and the combination of such logarithmic corrections with the standard approaches of fixed-order calculations and a parton shower, in order to get the best possible predictions. The project will therefore interface with many of the available tools and calculations used in particle physics phenomenology.

Project description will appear shortly.  View Nigel’s recent work.