Long-Term Collaborative Autonomous Monitoring of Energy Infrastructure Using Airborne Platforms for High Fidelity Scene Reconstruction

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. Find out more about the RAINZ CDT Training principles.

PhD Project Overview

• Cohort research challenge:Long-term autonomous monitoring and maintenance of assets
• Year 1 MSc Course:MSc Machine Learning or MSc Robotics
• Year 2 – 4 PhD Location: University of Glasgow

Project Abstract

This project addresses the challenge of long-term autonomous monitoring of critical energy infrastructure required for the transition to Net Zero. It focuses on developing a collaborative aerial autonomy framework centered on scene reconstruction under uncertainty as the basis for informed decision-making and persistent asset monitoring. A team of autonomous platforms will coordinate to collect visual and geometric data, producing probabilistic, multi-temporal reconstructions of infrastructure such as wind farms, solar arrays, and power networks. These reconstructions will form a shared information representation, enabling agents to reason about uncertainty, information gaps, and system confidence when planning sensing actions.

The research will investigate how uncertainty-aware reconstruction can drive autonomous decision-making, including when and where to revisit assets, how to allocate sensing resources, and when additional sensing modalities are required. A key component is the incremental updating of digital twins using partial, uncertain observations, supporting near-real-time condition assessment and predictive maintenance. The project will primarily use simulation environments to develop and evaluate scalable algorithms for multi-agent coordination, active perception, and probabilistic mapping. It aims to deliver robust, interpretable autonomy frameworks that enable efficient, low-carbon monitoring of large-scale infrastructure over extended time horizons.

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 strong programming skills (Python, Julia, C++, or similar).

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 (refer to The University of Manchester - Key Dates for more details).

Funding Notes

Funding:

Successful applicants will be awarded a 4-year studentship covering:

• Tuition fees paid at Home student rate*
• A tax-free stipend to help with living costs, set at the UKRI minimum rate** (i.e., £21,805 for 2026/27), which increases annually in line with inflation
• A Research Training and Support Grant to cover travel expenses and project consumables associated with your research including conference attendance, secondments, and other research and training activities