Unified Framework for Reservoir Computing

You will work at the intersection of mathematics, physics, electrical engineering and AI, helping to develop a theory that explains how and why these systems work - and how to design better ones. Why apply for this PhD?

• Work on the next-generation AI hardware beyond traditional computing architectures.
• Gain a unique combination of skills in mathematics, machine learning, and photonics.
• Be part of a multidisciplinary research team spanning science and engineering.
• Access state-of-the-art laboratories and high-performance computing facilities.
• Gain experience by attending international conferences and training events.
• Develop skills highly valued in both academia and industry.
  
  You do not need experience in all the areas below; additional training will be provided. Enthusiasm and willingness to learn are essential.
Essential:
• A first-class undergraduate degree or a master's degree in Physics, Applied Physics, Electrical and Electronic Engineering, Mathematical Sciences, or a closely related subject from a recognised institution.
• A background in at least one of the following:
• Dynamical systems
• Photonics/Electromagnetics theory, design and simulations
• Machine learning mathematics and algorithms
• Numerical methods
• Programming skills (Python, MATLAB, or similar)
• Strong analytical and problem-solving skills.
• Good written and spoken English.
Desirable:
• Experience with photonic/electromagnetics design software.
• Familiarity with deep learning platforms (e.g. TensorFlow, PyTorch).
  
  Modern AI computing systems require large amounts of energy and computational power. Reservoir computing offers a promising alternative by using complex physical systems to perform tasks such as prediction, classification, and signal processing.
However, one major challenge remains: We still do not fully understand what makes a reservoir computing system perform well. This PhD project aims to answer this question. You will develop a unified mathematical theory and framework to study and explain how different reservoir systems work and how to design them for specific tasks. The project will combine:
• Mathematical modelling of dynamical systems;
• Computational photonics simulations;
• Comparison with real physical systems (especially photonic systems using light).
Facilities and research environment:
• High-performance computing facilities;
• Photonics and electromagnetics laboratories;
• Experimental platforms for optical (light-based) computing;
• A collaborative research environment across mathematics and engineering., An UKRI rate studentship is available for this project, covering home tuition fees plus a tax-free stipend., Cover letter explaining your research interests, relevant skills and experience, and why you are interested in this PhD project
• Academic transcripts (for both undergraduate and postgraduate degrees, if applicable)
• Copies of any publications (if applicable)