Skip to main content
School of Electronic Engineering and Computer Science
  1. School of Electronic Engineering and Computer Science
  2. PhD
  3. PhD studentships
  4. Autonomous Intent-Driven Optical Networks: A Unified Framework Integrating Deep Reinforcement Learning and Generative AI

Autonomous Intent-Driven Optical Networks: A Unified Framework Integrating Deep Reinforcement Learning and Generative AI

Supervisors: Dr Paul Anthony Haigh (https://www.qmul.ac.uk/eecs/people/profiles/haighpaul.html

Project Description

In collaboration with Kyndryl, this research project will propose a comprehensive framework for autonomous optical network management that seamlessly integrates intent-based control, deep reinforcement learning optimisation, and generative AI automation. The proposed system will enable network operators to specify technical objectives and specifications in natural language, which are then autonomously translated into optimised network configurations and real-time control actions through intelligent AI agents. This work aims to address the critical challenges of managing increasingly complex optical networks (huge growth in data traffic, non-linear signal impairments) while reducing operational overhead and improving performance in multi-vendor, multi-domain architectures.

Recent advances in large language models, reinforcement learning, and generative AI offer transformative potential for network automation. However, existing approaches remain fragmented, addressing individual aspects of network management in isolation rather than providing a holistic, autonomous solution.

This PhD research aims to develop and validate a unified agentic AI framework with the following specific objectives:

Primary Objective: Design and implement an end-to-end autonomous optical network management system that translates operator intent into optimised network actions through intelligent and localised AI agents.

Secondary Objectives:

1. Develop a natural language understanding module that parses operator intent and network policies

2. Create a deep reinforcement learning engine for real-time optimisation of physical layer parameters

3. Design generative AI agents for automated network configuration and troubleshooting

4. Integrate digital twin technology for safe policy exploration and validation

5. Demonstrate the framework on a multi-node optical testbed with commercial equipment

Prerequisites:

- A Master’s degree (Distinction or equivalent) or an expected completion of such qualifications before starting the PhD.

- A keen interest in building working systems

- Solid programming skills

The PhD studentship is funded by EPSRC Doctoral Landscape Award open to those with Home and International fee status. However, the number of students with international fee status who can be recruited is capped according to the EPSRC terms and conditions, so competition for international places is particularly strong. Awards are tenable for up to 3.5 years, and cover tuition fees and a maintenance stipend at the UKRI rate (c. £22,870 p.a. full-time, £11,435 part-time for 2025/26; and 2026/27 rates tbc).

How to apply:

Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas.

Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/.

The application should include the following:

- CV (max 2 pages)

- Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)

- Research proposal (max 500 words)

- 2 References

- Certificate of English Language (for students whose first language is not English)

- Other Certificates

Please note that in order to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For more information please see: (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)

Application Deadline: The deadline for applications is the 28th January 2026.

For specific enquiries contact Dr Paul Anthony Haigh at p.a.haigh@qmul.ac.uk.

For general enquiries contact Mrs. Melissa Yeo at m.yeo@qmul.ac.uk (administrative enquiries) or Dr. Arkaitz Zubiaga at a.zubiaga@qmul.ac.uk (academic enquiries) with the subject “EECS 2026 PhD scholarships enquiry”.

Back to top