PhD Studentship in Reservoir Computing

University of Nottingham

A Unified Framework for Reservoir Computing: From Theory to Real-World Systems

Location:Faculty of Science and Faculty of Engineering, University of Nottingham, UK

Start Date:1 October 2026

This PhD offers an exciting opportunity to explore reservoir computing, a new approach towards artificial intelligence that uses the natural dynamic behaviour of physical systems (such as light and electronics) to process information efficiently.

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.

Project description

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:

1. Mathematical modelling of dynamical systems;
2. Computational photonics simulations;
3. Comparison with real physical systems (especially photonic systems using light).

Facilities and research environment:

1. High-performance computing facilities;
2. Photonics and electromagnetics laboratories;
3. Experimental platforms for optical (light-based) computing;
4. A collaborative research environment across mathematics and engineering.

Candidate profile

You do not need experience in all the areas below; additional training will be provided. Enthusiasm and willingness to learn are essential.

Essential:

1. 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.
2. A background in at least one of the following:
   • Dynamical systems
   • Photonics/Electromagnetics theory, design and simulations
   • Machinelearning mathematics and algorithms
   • Numerical methods
3. Programming skills (Python, MATLAB, or similar)
4. Strong analytical and problem-solving skills.
5. Good written and spoken English.

Desirable:

• Experience with photonic/electromagnetics design software.
• Familiarity with deep learning platforms (e.g. TensorFlow, PyTorch).

Funding and eligibility

The project is fully funded by DSTL, due to funding requirement this studentship is only available for UK (home) candidates.

An UKRI rate studentship is available for this project, covering home tuition fees plus a tax-free stipend.

How to apply

Send the following documents to sendy.phang@nottingham.ac.uk

1. CV
2. Cover letter explaining your research interests, relevant skills and experience, and why you are interested in this PhD project
3. Academic transcripts (for both undergraduate and postgraduate degrees, if applicable)
4. Copies of any publications (if applicable)

Please use “PhD-RC-Framework application – [Your Full Name]” as email subject matter.

Shortlisted candidates will be invited for an interview to assess their suitability.

Supervisors:

Professor Gregor Tanner – School of Mathematical Sciences, gregor.tanner@nottingham.ac.uk

Dr Sendy Phang – Faculty of Engineering, sendy.phang@nottingham.ac.uk