Civil Engineering : Fully Funded EngD Engineering Studentship in A real-time PINN-based computa[...]

European Commission; United Kingdom; On-site; GBP 21,000; Full time

Job summary

A leading university in the UK is offering an EngD position focused on the integration of data and modelling for fusion power plants. The project aims to explore the use of Physics-Informed Neural Networks (PINNs) for complex engineering problems. Successful candidates will engage in a structured research program over four years, culminating in a thesis. This opportunity is open to both UK and international students with a strong engineering background.

Benefits

Full tuition fees covered

Annual stipend at UKRI rate

RTSG budget for project costs

Qualifications

• IELTS 6.5 Overall or equivalent required.
• Open to UK & International students.

Responsibilities

• Training and deep dive into data integration in Year 1.
• Implement a unified inverse and forward framework in Year 2.
• Comprehensive testing in Year 4.

Skills

Engineering

Data Integration

Inverse Modelling

Education

Undergraduate degree at 2.1 level

Job description

Organisation/Company Swansea University Department Central Research Field Engineering » Civil engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country United Kingdom Application Deadline 30 May 2025 - 23:59 (Europe/London) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The engineering components of the fusion process have a significant number of unknowns. We anticipate that significant unknown issues such as material properties, thermal stress behaviour and thermal behaviour will be uncovered as we build fusion power plants. The data will come in sparse measurements of material properties, temperature, and strains. Integrating such properties into a modelling platform is challenging as constructing the problem from sparse measurements requires some form of rapid inverse work to determine, for example, the property distribution across the entire component. This will be vital at the development stage of essential components such as the breeder blanket. Inverse modelling comes with uncertainty and multiple solutions. Our previous fundamental work demonstrates that PINNs can help here but require further research.

Although the PINNs can be expensive in complex problems compared to traditional computational methods, they provide two distinctive advantages when dealing with the complexities of the fusion component analysis. PINNs can use a unified platform for both inverse and forward analysis, and parameterisation can be carried out in one calculation, i.e., additional parametric variations of material properties can be incorporated as an additional feature. These two advantages are worth investigating in the context of the complexities of a breeder blanket.

The following objectives are the objectives for this EngD project:

• Year 1: Training and deep dive into data integration, literature survey and analysis of current state of the art of PINNs for combined forwards and inverse framework.
• Year 2: Implement a unified inverse and forward framework on fundamental problems and test the method for speed, accuracy and robustness.
• Year 3: Implement the framework into various blanket design analyses (current student’s work) and incorporate any available data.
• Year 4: Comprehensive testing of both the combined inverse and forward PINN framework on competitive blanket designs, introducing PINN-based parametric studies and thesis writing.

This project is at the interface between UKAEA engineering and computing groups. Swansea will act as the bridge between these groups to deliver the best design options by combining high-fidelity simulations with design needs.

IELTS 6.5 Overall (5.5+ each comp.) or Swansea University recognised equivalent.

Additional Information

This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £20,780 for 2025/26).

An RTSG budget is available for project costs. All costs associated with attending CDT training will be met by the CDT.

Eligibility criteria

Open to UK & International students.

Applicants for EngD must hold an undergraduate degree at 2.1 level (or Non-UK equivalent as defined by Swansea University) in Engineering or similar relevant science discipline.

English Language
IELTS 6.5 Overall (5.5+ each comp.) or Swansea University recognised equivalent. Full details of our English Language policy, including certificate time validity, can be found here.

Please note that the programme requires some applicants to hold ATAS clearance,further details on ATAS scheme eligibility are available on the UK Government website .

ATAS clearanceIS NOT requiredto be held as part of the scholarship application process. Successful award winners (as appropriate) are provided with details as to how to apply for ATAS clearance in tandem with ascholarship course offer.

Selection process

Please see our website for further information