Available PhD Projects

The list of studentship projects for the September 2026 intake is now closed, as applications for this round have ended.

For reference, the projects previously advertised represented a starting point for discussion. Applicants should note that the outlined projects were intended to initiate conversations about the specific research topic to be undertaken during the PhD. Projects are assigned after an offer has been accepted, and students have the opportunity at that stage to further refine their final project choice and topic in discussion with prospective supervisors.

Other PhD Opportunities

Similar PhD projects are available in other departments at UCL, some of which are (co-)supervised by our academic staff. You can explore these opportunities through the respective department websites listed below:

• High Energy Physics
• Astrophysics

• MSSL

   

Projects

Based on the project themes, supervisors, and the way the DIS CDT is structured across High Energy Physics (HEP), Astrophysics/Astronomy (Astro), and the Mullard Space Science Laboratory / wider space-science side, the currently listed projects broadly split as follows:

HEP

• COLLIDER/ATLAS + MACHINE LEARNING – Leveraging cutting-edge machine learning to understand the initial and future evolution of the Universe
• COLLIDER/ATLAS + MACHINE LEARNING – Advancing Machine Learning Techniques for Particle Tracking and Understanding the Universe with the ATLAS Experiment
• COLLIDER/ATLAS -  What happened 1ps after the Big Bang? Exploring Higgs pair production with the ATLAS experiment
• ACCELERATOR/NEUTRINOS – Building a neutrino detector with ML

Astro

• ASTROPHYSICS/COSMOLOGY – Machine Learning for Anomaly Detection in Astrophysical Data
• COSMOLOGY/SEISMOLOGY - Multi-fidelity and transfer learning for earthquake and cosmic structure inference
• ATOMIC/MOLECULAR PHYSICS/ULTRAFAST SCIENCE -  Machine learning in ultrafast process in molecules driven by intense infrared laser pulses

MSSL / Space Science / Computational Physical Sciences

• MACHINE LEARNING MOLECULAR DYNAMICS/SPECTROSCOPY/ENERGY MATERIALS – Machine Learning Molecular Dynamics and Spectroscopy of Solvated Geochemical Interfaces
• MODELLING POLARON DYNAMICS – Developing new methods to study dynamics of photo-excited systems
• PROPERTIES OF FUNCTIONAL OXIDES – Using machine learning and atomistic simulations to improve AI technologies)
• ELECTRON MOLECULE COLLISIONS/MACHINE LEARNING/LARGE LANGUAGE MODELS – Democratising High-Precision Physics: An LLM-Driven Agent for Quantemol-EC
• MACHINE LEARNING FOR CHEMISTRY/MATERIAL SCIENCE – Machine Learning for correlated electronic structure theory