Smart optical design: AI tools for the next generation of cinematic camera lenses

This project aims to develop a computational framework combining computer vision, computer graphics, and machine learning to accelerate and improve the design and simulation of camera lenses. Traditional lens design is slow, labour-intensive, and relies on expert-led iterative methods. We propose instead to learn mappings between lens design parameters and image-quality metrics using data-driven, differentiable rendering models. The framework will support two modes: forward simulation (predicting the imaging output of candidate lens configurations) and inverse design (proposing lens architectures to meet a target imaging/perceptual objective).

You will need to meet the minimum entry requirements for our PhD programme.

We are seeking a highly motivated and technically strong candidate with a background in computer science, electronic engineering, applied mathematics, physics, or a closely related discipline.

The ideal candidate will have:

• A first-class or high 2:1 honours degree (or equivalent) and preferably a Master’s degree in a relevant field.
• Strong mathematical foundations, particularly in linear algebra, optimisation, probability, and numerical methods.
• Experience in computer vision, computer graphics, computational imaging, or machine learning.
• Excellent programming skills in Python and/or C/C++, with experience using machine learning frameworks such as PyTorch or TensorFlow.
• Familiarity with rendering, simulation, or physics-based modelling (e.g., ray tracing, differentiable rendering, optics, or photometric modelling) is desirable.
• An interest in interdisciplinary research at the interface of AI, computational imaging, and industrial optical design.

We particularly welcome applicants who are excited about translating cutting-edge AI research into real-world engineering systems through close collaboration with industry. Strong problem-solving ability, creativity, and the capacity to work both independently and within a collaborative research team are essential.

Prior experience with optical systems, inverse problems, generative models, or high-performance computing would be advantageous but is not required.

Open to any UK or international candidates.