(c) Neil Holmes - offshorewind

Autonomous Prediction and Scheduling of Operations and Maintenance for Offshore Wind Farms

About this project

The UK is leading the world in the deployment of offshore wind energy and Hull is at the centre of the industry, with the largest companies in the sector based in the region (Siemens Gamesa Renewable Energy and Ørsted). The University of Hull is working with these companies and other leading organisations to support the development of the industry through Aura.

The rapid development of the offshore wind (OSW) industry in UK waters has opened up many challenges and opportunities for research and innovation.

The rapid development of the offshore wind (OSW) industry in UK waters has opened up many challenges and opportunities for research and innovation. This is particularly true of Operations and Maintenance (O&M) where novel approaches are required, and where many innovation opportunities exist. O&M accounts for 20-30% of the lifetime cost of electricity generated from OSW and is a priority within the Industrial Strategy Green Paper and for funding agencies. To exploit these opportunities for research funding, the University of Hull has recently agreed with the Offshore Renewable Energy Catapult (OREC) to establish a joint Offshore Wind Operations and Maintenance Centre of Excellence (O&M CE) with the purpose of supporting innovation to help reduce the lifetime cost of electricity and promote the UK supply chain. The Centre of Excellence will develop a portfolio of activities ranging from early stage research to nearer market development based around a series of roadmaps developed in collaboration with the OSW industry. Through discussion with OREC, six areas of focus have been identified: Human free O&M, Human factors in O&M, Data driven O&M decision making, Offshore logistics, planning and risk, Lifetime asset management and Whole life supply chain modelling. The recently awarded PhD cluster of 4 scholarships will conduct early stage research addressing these six priority areas, which will feed a pipeline of innovation that ensures the long term sustainability of the Centre of Excellence.

The successful candidate will be based in the appropriate academic school but will benefit from opportunities to collaborate with AURA, the O&M CE, and both of the Energy and Environment and the Logistics Institutes.

Autonomous Prediction and Scheduling of Operations and Maintenance for Offshore Wind Farms 

This PhD will focus on the use of data analytics and prediction for OSW O&M. Initially the PhD will apply domain-general data analytics - such as deep learning – and identify the family of algorithms best suited to failure prediction and maintenance scheduling in OSW O&M. Subsequently, it will investigate domain-tailored approaches combining disparate datasets, including real-time images and videos, met-ocean forecasts and turbine data. The PhD project will build upon and collaborate closely with the existing DSTL funded Hazardous Environment Autonomous Resupply (HEAR) project, which is developing a software framework for predicting, monitoring and route planning resupply using autonomous vehicles. The intended outcomes of the research will be utilised to help drive down the cost of offshore wind energy through operational and maintenance cost savings identified using a version of the HEAR framework suitable for OSW O&M. Our research will make use of the University’s new High-Performance Computing facility Viper, which is equipped with state of the art equipment for parallel processing, high-memory computation and GPUs, and is ranked amongst the top 7 in the North of England.


If you have any queries, please email contact Dr James Walker.

Next steps


To celebrate the University's research successes, the University of Hull is offering a full-time UK/EU PhD Scholarship or International Fees Bursary.

Entry requirements

Applicants should have excellent programming skills and at least a 2:1 degree in Computer Science or a related discipline. Previous experience of one or more of the following areas is highly desirable: machine/deep learning, high performance computing or data analytics. 

Applicants should have at least a 2.1 undergraduate degree or Master’s degree in the disciplines noted above, together with relevant research experience.

How to apply

Applications for scholarship consideration at the University of Hull should be made through the Postgraduate Application system.

On the second page of your application, please select “Graduate Scholarship” as the type of scholarship you are applying for. 

Applicants are strongly encouraged to first identify and contact a potential supervisor. 

Application deadline: Monday 19 February