Atomic sensors are exquisitely sensitive. We aim to model and build a new generation of atomic sensors to measure magnetic fields, rotation and dark matter. 

Optical cavities are widely used in physics and precision measurement.  This project will explore the use of modern machine learning methods for the control of suspended optical cavities.

This project aims to develop a three-mirror coupled optical cavity system with automated alignment and control. Machine learning will be used to identify optical modes and optimize cavity operation, enabling advanced studies in precision optical control and interferometry.

Turbulence is one of the most important unsolved problems in modern physics, underpinning universal phenomena from galactic formation to heat and pollutant transport in our atmosphere and oceans. This project seeks to theoretically investigate turbulence in superfluids, and introduce methods of controlling the system dynamics using quantum feedback control.

This project aims to use a machine learning algorithm to perform beam alignment in an optics experiment. It would involve mode-matching two optical beams using motorised mirror mounts. Additional degrees of freedom like lens positions and beam polarisation can be added later.

Machine learning based on deep neural networks is a powerful method for improving the performance of experiments.  It may also be useful for finding new physics.