PhD Studentship: Advanced Analytics for Wind Energy Systems
University of Strathclyde
| Qualification Type: | PhD |
|---|---|
| Location: | Glasgow |
| Funding for: | UK Students |
| Funding amount: | Fully funded four-year studentship |
| Hours: | Full Time |
| Placed On: | 3rd June 2025 |
|---|---|
| Closes: | 1st September 2025 |
Programme Overview
This PhD is part of the Engineering Hydrogen NetZero (EnerHy) Centre for Doctoral Training (CDT), a newly established EPSRC-funded initiative focused on advancing research and training in green hydrogen and wind energy. The University of Strathclyde leads the wind energy research and training components of the programme. Funded by Natural Power and the EPSRC, this four-year PhD studentship, commencing in October 2025 as part of the second EnerHy intake, is based at the University of Strathclyde and centres on advanced analytics for wind energy data.
Research Project Overview
The wind energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA data, energy yield data, condition monitoring system data, nacelle lidar data, maintenance data etc), and decommissioning. Despite the wind energy sector’s success in data collection, significant untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis, feature extraction etc, to enhance data utilisation and improve existing models or develop new models for wind energy systems. The candidate will be hired knowing the general area of the PhD will relate to “advanced analytics of wind energy data” and the specific research focus and detailed research questions will be co-developed by the successful candidate and Natural Power during the first year of the programme.
Entry Requirements
This fully funded four-year studentship (covering fees, stipend, and travel budget) is open to candidates with a first-class or upper second-class degree (or equivalent) in Computer Science, Data Science, Machine Learning, or an Engineering/Science discipline with a data science/analytics component. To apply, submit a CV, cover letter, and academic transcripts to j.carroll@strath.ac.uk and drew.smith@strath.ac.uk.
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