Human-in-the-loop machine learning for drone-assisted Structural Health Monitoring

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience, a partnership between the Universities of Durham, Hull, Loughborough and Sheffield. The successful applicant will undertake six months of training with the rest of the CDT cohort at the University of Hull before continuing their PhD research at Loughborough University. Inspections of offshore wind turbines, such as identifying damage or ice on turbine blades, anticipating its effects and making decisions on maintenance and repair, as well as estimating remaining useful life (RUL), is an important part of extending the lifetime of a wind turbine as well as the power that can be generated from it.

While both tasks are often driven by experts, public data on environmental, meteorological or physical conditions, in combination with satellite and/or climate data, can help make predictions for new, unseen conditions. The latter is particularly relevant when data is sparse. While public data exists on general environmental conditions and turbine power yield, data around specific combinations of operational and environmental conditions is not always readily available — this is particularly the case for new generations of floating or far offshore turbines, which are much harder to reach and inspect than previous generations much closer to shore, and for which less historical data is available.

This project aims for two key research advances: first, the development of a new human-in-the-loop active learning framework [2, 3], which uses conversational AI to negotiate key decisions related to turbine inspection and maintenance with a human expert [4, 5]. This can be based on a deep reinforcement learning framework, which interactively optimises key performance indicators in the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including UAVs (e.g., drones) that may be used in turbine inspection.

How to apply Applications are now open for October 2026 entry and will be considered on a rolling basis, and we therefore strongly encourage applicants to apply as early as possible. Shortlisted candidates may be invited to interview as applications are received and offers may be made before all applications are reviewed and if a scholarship is filled any future scheduled interviews may need to be cancelled. The scholarships will remain open until all places are filled.

We warmly welcome applications from all eligible candidates, and we particularly encourage applications from women, mature students, disabled applicants, and marginalised Black and Asian students with home fee status, who remain under represented in our cohorts. If you need additional time to prepare your application due to disability, caring responsibilities, health related needs, or other circumstances linked to protected characteristics, you may submit a sho