Advancing accelerator science through data-intensive research and training

The Liverpool Centre for Doctoral Training in Innovation in Data Intensive Science (LIV.INNO) has made significant progress in applying data-intensive methods to accelerator research. This contribution presents research outcomes from the center with a focus on two key projects. The first focuses on optimizing 3D imaging for medical and industrial applications, integrating Monte Carlo simulations and advanced collimation techniques to enhance low-dose, portable X-ray systems, with implications for wider accelerator diagnostics. The second lever-ages deep learning models to reconstruct transverse beam distributions at CERN, addressing challenges in image distortion from multimode optical fibers under high-radiation conditions. The results are connected with wider progress made in machine learning and artificial intelligence for particle accelerators. Furthermore, the paper summarizes the outcomes of several key LIV.INNO events: the STFC Summer School on Data Intensive Science, the LIV.INNO 2024 Industry Showcase and the 2025 AI for Innovation Summit.