Background: I completed my Electrical and Electronic Engineering Undergraduate at the University of Hull in the June of 2021. During this time, I engaged in multiple embedded systems projects which focussed on applications such as data logging and mechanical system control. In my dissertation I utilised supervised machine learning algorithms, on EEG datasets, to differentiate between imagined and physical hand movement. Over the summer of 2021, I interned at the University of Hull, where I worked in the development of sensor systems to validate models of wound dressings for a Smith + Nephew project.
Research Interests: My current academic work has been in developing performance analysis sensor systems, for biological applications, which readily translates into sensor design for wind turbine systems. I am also eager to explore how machine learning techniques can be integrated into control or analysis systems to improve a model’s operation and performance.
Why you applied for the Aura CDT: I applied to Aura because it offered the opportunity to study the challenges facing an economically and socially significant industry. Initially from a multidisciplinary perspective to provide a holistic view of the industry, and then a more refined perspective through a research project. I felt this study approach would give me the greatest opportunity to explore and understand the green industry before tackling one of its complex issues.
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