Development of Novel Statistical Methods and Application to Searches for New Physics Using the ATLAS Detector at the LHC

This PhD opportunity at Royal Holloway, University of London, focuses on the development of novel statistical methods and their application to searches for new physics using the ATLAS detector at the Large Hadron Collider (LHC). The RHUL ATLAS group is renowned for its expertise in experimental and computational physics, particularly in areas such as Higgs boson and top quark studies, as well as advanced statistical techniques and data acquisition systems. The group is actively involved in the upcoming upgrades for the High-Luminosity LHC, providing a dynamic research environment for students. The successful candidate will work on creating innovative statistical models for data analysis, including handling uncertain error parameters, measuring differential distributions through deconvolution (unfolding), and applying machine learning techniques for statistical tests, multivariate regression, and density estimation. These methods will be directly applied to physics analyses using data collected by the ATLAS detector, with a focus on searching for new processes in areas such as top-quark or Higgs boson properties. The research will utilize data from the next data-taking run (run 3) at 14 TeV proton-proton collisions. PhD students in the RHUL ATLAS group also contribute to the development of statistical software for the ATLAS Collaboration and participate in the operation of the ATLAS detector at CERN. This provides a unique opportunity to gain hands-on experience in both software development and experimental physics at one of the world's leading particle physics facilities. Funding for this position includes the possibility of Long Term Attachment funds, allowing the student to work at CERN for up to 18 months during their PhD. This enhances the research experience and provides valuable international collaboration opportunities. Applicants should have a strong background in physics, mathematics, or a closely related discipline, with experience in statistical methods, data analysis, and computational techniques being highly desirable. Applications are accepted year-round via the Royal Holloway Applicant Portal, and no research proposal is required. For more information and to apply, visit the project page linked below.