Background: I graduated from the University of Sheffield in 2014 with an integrated Masters in Mechanical Engineering, which taught me fundamental principles across the range of engineering disciplines. Since then, I have worked as an engineering consultant in the conventional power sector. Here I have developed a passion for applying statistics and/or physics-based modelling techniques to drive efficiency and environmental improvements using time series data.
Research Interests: My research interests are in applying artificial intelligence and machine learning techniques to improve fault diagnostics and wind farm yield, to support lowering the levelised cost of energy.
Why I applied to the Aura CDT: The Aura CDT scholarship is a great opportunity to support my desired transition into the renewable energy sector, whilst also providing a platform to contribute original knowledge in the fight to limit the effects of climate change. It will also be fascinating to learn from the broad knowledge base of the cohorts and build a complete picture of the wider challenges with offshore wind turbines.
My research: I am researching Population-based offshore wind farm fleet health monitoring and performance prediction
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