To create a fusion reactor, therefore, relevant components must go through rigorous testing before they can be deployed. Due to the challenges associated with testing, any additional information to understand these components would be extremely useful. In the proposed studentship, novel fundamental knowledge will be developed using both forward and inverse machine learning approaches to deliver new digital twin models. These digital twin models will seamlessly integrate both real and synthetic data into high performance deep learning algorithms.
The successful candidate will have a good undergraduate degree in a relevant subject, e.g. physics, engineering or computer science. A postgraduate degree with relevant experience in the topics of this PhD is an added advantage. Previous specialisation in machine learning and/or computational mechanics will allow the student to rapidly start the work. The first year of the PhD will mostly be spent on testing novel machine learning methods for their suitability. The second year will allow the student to move into digital twin design and eventually leading to integration of the model into the workflow at UKAEA in the third year.
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