Techno-economic optimisation of robotic inspection for circular offshore energy assets

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, Glasgow, or 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 Renewable Energy and Clean Technology
• Year 2 – 4 PhD Location: The University of Manchester

Research Abstract: Offshore wind and marine energy assets operate in harsh, inaccessible environments where manual inspection is costly, hazardous, and infrequent. This inspection deficit means end-of-life decisions rely on conservative design lifetimes rather than actual asset condition, resulting in premature decommissioning, lost operational value, and suboptimal material recovery, undermining circular economy objectives critical to sustainable net zero pathways. This PhD project will integrate robotic inspection technologies with techno-economic modelling to enable condition-based circular economy decision-making for offshore energy infrastructure. The project will: (1) evaluate autonomous inspection platforms, e.g. aerial drones, climbing robots, and remotely operated underwater vehicles, for capturing degradation data across turbine blades, towers, foundations, and subsea cables; (2) develop a machine learning approach to translating multi-modal inspection data into remaining useful life predictions; and (3) create a dynamic techno-economic model linking real-time condition assessments to optimal intervention strategies (repair, refurbishment, remanufacture, or recycling) under different energy system scenarios. The model will use industry case studies, quantifying how robotics-enabled predictive maintenance affects lifecycle costs, critical mineral demand, and circular recovery infrastructure requirements.

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 and to Overseas students who do not require an ATAS certificate.

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., £20,780 for 2025/26), 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

Additional funding is available to support a range of CDT activities, such as secondments or institutional visits, the purchase of additional specialised equipment, and an accessibility fund to support students with specific needs (e.g., caring responsibilities) when attending conferences or other required activities.

*A limited number of CDT studentships may be awarded to international students each year. We strongly encourage international applicants to discuss tuition fee waivers during the interview stage, so that potential fee reductions or additional scholarship support through the host university can be explored. Any waiving of international fees will be considered on a case-by-case basis by the host institution.

**TechExpert: As part of the UK Government’s TechFirst skills programme, successful Home applicants to Cohort 2 of the RAINZ CDT will receive a £10,000 per year enhancement to their UKRI minimum stipend.

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 (The University of Manchester - Key Dates).