Fully-Funded EPSRC and UKAEA PhD international awards in UK

  • Phd
  • Tuition fees + ¬£15,609 p.a. stipend
  • 31 May 2021
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This scholarship is funded by the EPSRC Doctoral Training Partnership and UK Atomic Energy Authority (UKAEA).
Start date: October 2021
Subject areas: Machine learning, computational mechanics, data, digital twin
Project supervisors: Professor Perumal Nithiarasu (Swansea), Dr Llion Evans (Swansea/UKAEA), Dr Michelle Tindall (UKAEA)
Project description: 
The inside of a fusion reactor is one of the most challenging environments known about, with temperatures ranging from the hottest in the solar system (100,000,000 ¬įC at the centre of the plasma) to the coolest (-269 ¬įC in the cryopump) all within a few metres, coupled with electro-magnetic loads and irradiation damage. This has already been achieved for short periods of time at JET, the world‚Äôs largest fusion device located at Culham Centre for Fusion Energy (UKAEA), UK. But one of the greatest engineering challenges of the 21st century will be to construct a machine that can operate under these extremes routinely and produce commercially viable energy.
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.


Candidates should hold a minimum of an upper second class (2:1) honours degree and/or a master's degree (or its equivalent) in engineering, computer science, mathematics, physics, or a subject area related to the project.
A strong background in numerical methods or machine learning is required.
Knowledge/experience of programming in compiled languages (e.g. C, C++, or Fortran) and interpreted languages (e.g. Python) is essential and CUDA is desirable.
We would normally expect the academic and English Language requirements (IELTS 6.5 overall with 5.5+ in each component) to be met by point of application. For details on the University‚Äôs English Language entry requirements, please visit ‚Ästhttp://www.swansea.ac.uk/admissions/english-language-requirements/
This scholarship is open to candidates of any nationality. For more information on residency requirements, please visit the EPSRC website.
NB: If you are holding a non-UK degree, please see Swansea University degree comparisons to find out if you meet the eligibility. 
If you have any questions regarding your academic eligibility based on the above comparison, please email 
pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in. 


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This scholarship covers the full cost of tuition fees and an annual stipend of £15,609 for 3 years. 
There will be additional funds available for research expenses.


To apply, please complete and submit the following documents to engscholarships@swansea.ac.uk, and address the email for your application as 'Novel machine learning based twin applications in fusion research  - Professor Perumal Nithiarasu':

Informal enquiries are welcome, please contact Professor Perumal Nithiarasu (
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