PhD Studentship: Artificial Intelligence for Structured Light Optimisation at the Nanoscale

Project Supervisor - Dr Kayn Forbes

Structured light – beams with tailored phase, polarisation, and topological structure – has revealed remarkable new ways to manipulate matter. Yet, even the most sophisticated beams used today represent only a fraction of the possible optical configurations. This PhD project aims to unlock the full physical potential of structured light by developing artificial intelligence (AI) systems that learn how to design and optimise complex optical fields for real-world applications. 

Working within the Forbes Group at the University of East Anglia (UEA), the project will combine theoretical photonics, computational modelling, and physics-informed machine learning. You will create AI algorithms trained on Maxwell-based simulations to identify the beam structures that maximise measurable effects – such as optical chirality, momentum transfer, and local field enhancement – at the nanoscale. These tools will provide new routes to optimise light-matter interactions for cutting-edge applications in molecular detection, nanoscale imaging, and optical manipulation. 

Beyond algorithm development, the project will deepen our physical understanding of how topological and spin-orbit features in light can be tuned and exploited. Collaborations with leading international partners across the US, Europe, and Africa will provide opportunities for joint theoretical-experimental studies and global engagement in this rapidly growing field. 

This project is suited to a motivated student with a background in physics, chemistry, or computer science, who wants to use computational and theoretical tools to harness AI for discovering and optimising complex structured light at the nanoscale. 

Entry Requirements

Acceptable first degree subjects - Chemistry, Physics, Computer Science, or Maths. 

Minimum academic requirement - A 2:1 undergraduate degree. 

Start Date: 1 October 2026 

Additional Funding Information

This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years.

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