DoS Dena Bazazian: (dena.bazazian@plymouth.ac.uk)
2nd Supervisor: Deborah Greaves, (deborah.graves@plymouth.ac.uk)
3rd Supervisor: Asiya Khan, (asiya.khan@plymouth.ac.uk)
4th Supervisor: Pablo Borja Rosales, (pablo.borjarosales@plymouth.ac.uk)
Applications are invited for a 3.5-year EPSRC funded UDLA PhD studentship.
The studentship will start on 1st October 2025.
Project Description
Offshore wind turbines are pivotal in driving the global transition to renewable energy; however, maintaining these critical assets poses substantial challenges. Advances in computer vision and deep learning offer a paradigm shift in automating turbine inspections, enhancing efficiency and accuracy. This project aims to develop an advanced drone-based system employing both aerial and underwater drones, combined with state-of-the-art computer vision to detect and predict structural damage to wind turbines, ensure enhanced predictive maintenance and promote sustainable renewable energy operations.
The objectives of this project are following:
- Aerial Damage Detection and Classification: Design computer vision algorithms for aerial drones to detect and classify structural issues such as cracks and erosion on turbine blades and surfaces.
- 3D Reconstruction Models: Develop accurate 3D reconstruction models to measure and analyse the geometric properties and severity of aerially detected damage, enabling precise structural assessments.
- Underwater Inspection and Biofouling Detection: Deploy underwater drones with advanced imaging systems to monitor damage such as biofouling, algae, barnacles, marine growth, and structural wear on subsea cables and foundations.
- Predictive Maintenance Insights: Integrate AI-driven analytics to generate actionable maintenance plans, optimise repair schedules, reduce unplanned downtime, and extend turbine lifespan.
- Sustainability and Cost-Efficiency: Enhance operational efficiency by lowering inspection costs and improving the long-term reliability of offshore wind farms.
By integrating aerial and underwater drones, this project provides a comprehensive solution for wind turbine inspections and will foster interdisciplinary and impactful publications in fields such as offshore wind energy, computer vision and AI applications.
Eligibility
Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification. Applications from both UK and overseas students are welcome.
The studentship is supported for 3.5 years and includes full Home tuition fees, Bench fee plus a stipend of £20,780 per annum 2025/26 rate. The studentship will only fully fund those applicants who are eligible for Home fees with relevant qualifications. Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates. The international component of the fee may be waived for outstanding international applicants.
There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.
NB: The studentship is supported for 3.5 years of the four-year registration period.
The subsequent 6 months of registration is a self-funded ‘writing-up’ period.
If you wish to discuss this project further informally, please contact:
Dr Dena Bazazian (dena.bazazian@plymouth.ac.uk).
To apply for this position please click on the Apply button above.
Please clearly state the name of the studentship that you are applying for on your personal statement.
Please see here for a list of supporting documents to upload with your application.
For more information on the admissions process generally, please contact:
research.degree.admissions@plymouth.ac.uk
The closing date for applications is 12 noon on 28th March 2025.
Shortlisted candidates will be invited for interview shortly thereafter.
The studentship is supported for 3.5 years and includes Home tuition fees plus a stipend of £20,780 per annum 2025-26 rate
