AI-empowered Zero Touch Wireless Network Management for Reliable and Efficient Autonomous Robot Fleets

About the Project

About the RAINZ CDT

The EPSRC Centre for Doctoral Training in Robotics and Artificial Intelligence for Net Zero is a partnership between three of the UK’s leading universities (The University of Manchester, University of Glasgow and University of Oxford).

Robotics and Autonomous Systems (RAS) is an essential enabling technology for the Net Zero transition in the UK’s energy sector. However, significant technological and cultural barriers are limiting its effectiveness. Overcoming these barriers is a key target of this CDT. The CDT’s research projects will focus on how RAS can be used for the inspection, maintenance and repair of new infrastructure in renewables (wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the decarbonization of existing maintenance and decommissioning of assets.

We are seeking motivated and curious graduate scientists and engineers who are interested in developing new skills and have a desire to help increase use of RAS to support the decarbonisation of the energy sector. RAINZ CDT students will play an important role in advancing this rapidly growing area of science and engineering.

Programme structure (1+3)*

Year 1 (Taught component):All students spend the first year at The University of Manchester undertaking taught MSc studies and bespoke CDT training. Students must achieve an average of 65% or higher in their MSc assessments to be considered for progression to the PhD component of the programme.

*Note: Students do not graduate with an MSc degree as the summer period is spent undertaking a CDT summer school rather than an MSc Dissertation.*

Years 2 – 4 (PhD research):Students are based at the host institution to undertake their PhD research (i.e., either The University of Manchester, University of Glasgow, or University of Oxford), which will be complemented by a comprehensive cohort-wide training and employability programme.

The RAINZ CDT programme follows a cohort-based training and research designed to ensure that graduates are not only subject matter experts, but also equipped with highly valuable skills in teamwork, sustainability, EDIA and wellbeing, industrial engagement, and commercialisation. Each cohort tackles an industry co-created, cross-sector challenge that requires a multi-disciplinary team of engineers and scientists to solve it. Researchers explore different aspects of the challenge, which are then integrated through the RAINZ CDT annual research sprints.

PhD Project Overview

• Cohort research challenge:Long-term autonomous monitoring and maintenance of assets
• Year 1 MSc Course:MSc Communications and Signal Processing
• Year 2 – 4 PhD Location: University of Glasgow

Project Abstract

The rapid advancement of autonomous robot fleets demands efficient, scalable, and intelligent wireless network management to ensure reliable coordination and timely mission completion. Traditional network management approaches are inadequate for meeting the stringent requirements of coordinating complex, large-scale, and highly dynamic robot fleets. This PhD research aims to develop an AI-driven Zero Touch Management (ZTM) system for wireless networks coupled with an intelligent coordination strategy, designed specifically to meet the unique needs of autonomous robot fleets. By harnessing the power of AI/ML and software-defined networking (SDN), and distributed learning methodologies, the research will focus on creating self-configuring, self-optimizing, and self-healing mechanisms for real-time network management and robot coordination. The project will address key factors such as seamless connectivity, low latency, and enhanced reliability, accounting for heterogeneous mobility, data traffic patterns, communication resources, and the real-time operational demands of robot fleets. Core areas of investigation include dynamic network topology control, interference mitigation, mobility-aware resource allocation, fault tolerance, and adaptive coordination. Furthermore, techniques such as adversarial training, anomaly detection, and secure model aggregation will be investigated to safeguard the robots and network infrastructure from malicious activities.

The innovative solutions developed through this research will enable more efficient and reliable operations for autonomous robot fleets, minimizing downtime, enhancing safety, and reducing operational costs through intelligent, automated network management and coordination. The efficacy of the developed solutions will be demonstrated through both ROS-based simulations and lab experiments involving (heterogeneous) robot fleets for missions such as automated inspection

Eligibility

Applicants should hold a First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics, Mathematics, or a related discipline. Applicants should also demonstrate evidence of programming experience.

Please note this project is open to Home students.

EDIA

Equality, diversity, inclusion, and accessibility are fundamental to the success of the RAINZ CDT, and are central to all our activities. We recognise that a diverse research community enhances creativity, productivity and research quality, and contributes to greater societal and economic impact. We value applications from individuals of all backgrounds and identities.

We are committed to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is designed to minimise unconscious bias, providing equal opportunities for all applicants.

How to Apply

Applications should be submitted through the RAINZ CDT website, where further information about the CDT is also available. Informal enquiries can be made by emailing rainz@manchester.ac.uk.

The deadline for submitting the RAINZ CDT application form is 5:00 pm, Friday 15 May 2026. Applications received after this deadline will not be considered.

Start Date:Monday 21 September 2026