PhD Studentship: Mechanical Engineering, Fusion, Digital: An AI Enhanced Modelling of Coupled Tritium Breeding and Heat Exchange for Fusion Breeder Blankets
Swansea University, United Kingdom
- Institution
- Swansea University
- Country
- United Kingdom
- Position type
- PhD
- Subject area
- Engineering
- Salary
- GBP 21805.00 YEAR
- Location
- Swansea, Wales, United Kingdom
- Funding
- Fully funded position
Want to be the strongest applicant for this position?
Book a free 15-minute call with our team. We will look at your profile against what Swansea University is actually looking for, and tell you honestly where you stand and what to strengthen before you apply.
Book a free call about this position →No cost, no obligation. Mentors who have secured funded PhD positions themselves.
How our PhD admission mentorship works →
We mentor STEM applicants only. If your research aspirations are outside STEM subjects, you're very welcome to use this board and apply directly; we just aren't the right people to mentor you.
About this position
In future fusion power plants operating with a closed-loop fuel cycle, breeding a sufficient quantity of tritium is essential for sustained power generation. A key performance metric is the Tritium Breeding Ratio (TBR), which depends on several tightly coupled factors, including breeder blanket design, plasma-facing surface area, neutron transport, material composition, and cooling performance. Since cooling is also intrinsically linked to heat extraction, structural integrity, and irradiation-induced material damage, TBR optimisation represents a highly coupled neutronic–thermomechanical challenge.
This PhD project will investigate this coupled problem within the context of UK-specific tokamak reactor designs, working collaboratively with other researchers and doctoral students across related areas. The primary focus will be on neutronics and optimisation of TBR within realistic fusion operating conditions.
Initially, the research will employ Monte Carlo neutronics methods using tools such as OpenMC (or equivalent) to model neutron transport and tritium breeding behaviour within breeder blanket configurations. The project will then extend toward accelerated predictive methodologies using machine learning and AI approaches, including surrogate modelling and large language model (LLM)-assisted information extraction from openly available international fusion datasets and literature.
Applications may be submitted in Welsh and any application submitted in Welsh will be treated no less favourably than an application submitted in English. Please refer to the University’s Welsh Language Policy on Awarding Grants.
Apply directly to Swansea University
Emerging Scholars Council is not the employer and does not recruit for this position. It is advertised by Swansea University, and your application goes to them. We help students prepare and strengthen their applications.
Go to the official application page →