Civil Engineering: Fully Funded PhD studentship in AI-Assisted Mesh Generation and Adaptation f[...]

Swansea University; United Kingdom; On-site; GBP 21,000; Full time

Job summary

A prestigious university in the UK is offering a PhD position focusing on machine learning and engineering simulations. The role involves working on innovative approaches to mesh generation and adaptation, critical for enhancing simulation reliability. Candidates should have an undergraduate degree at 2.1 level in Engineering or a similar relevant science discipline. This opportunity includes a scholarship that covers the full tuition fees and an annual stipend. Excellent communication skills and the ability to work with complex geometries are essential.

Benefits

Full cost of tuition fees covered

Annual stipend at UKRI rate

Qualifications

• PhD applicants must have a relevant undergraduate degree with a minimum classification of 2.1.
• Strong understanding of machine learning and geometry processing is required.
• Demonstrated ability in simulations and practical applications is essential.

Responsibilities

• Work on machine learning processes to refine mesh generation.
• Explore industrial simulation and geometry processing in realistic configurations.
• Develop automated solutions for mesh adaptation and generation.

Skills

Machine Learning

Geometry Processing

Simulation

Education

Undergraduate degree at 2.1 level in Engineering or similar relevant science discipline

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 2 Feb 2026 - 23:59 (Europe/London) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2026 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

In many engineering simulations, the accuracy and efficiency of the solution depend critically on how the mesh is distributedrelativeto the underlying physics. Featuressuchas boundary layers, shocks, vortices, thermalgradientsand structural stresses often occur in regions that areclosely linkedto the geometry of the problem. In current industrial workflows, these phenomena are commonly captured either by globally over refining the mesh, which is computationally expensive and environmentally inefficient, or by running multiple successive simulations to iteratively adjust the mesh. Both approaches raise computational cost, energyconsumptionand turnaround time, placing increasing pressure on sustainability targets. Understanding how geometric changes influence the flow, thermal or structural responseremainsa major challenge, and traditional mesh spacing strategies struggle to capture the complex, nonlinear ways that geometry shapesmultiphysicsbehaviour, leading to either unnecessary refinement or a loss of fidelity in critical regions.

Machine learning provides a promising route to capture these relationships more systematically byidentifyinghow local geometric featuresdeterminethe resolutionrequiredfor reliable prediction. A central goal of this project is to learn how solution fields and mesh resolution requirements vary with geometric change, enabling sensitivity informed meshes that adapt to both physical behaviour and geometric context. The project will develop a machine learning framework that learns the link between geometric variation, coupled physical responses, solution sensitivityand mesh spacing requirements. The resulting tools will support automated mesh generation and adaptation, reduce manual tuning and improve the reliability of simulations involving geometry driven behaviour across multiple physical models.

As the PhD researcher on this project, you will work at the intersection of machine learning, geometryprocessingandindustrial simulation. You will have the opportunity to explore realistic engineering configurations and gainexpertisein areas that are rapidly growing yet still rare across the simulation community, including AI-assisted mesh generation and adaptation for industrial simulation. The skills developed through this work will align strongly with careers in scientific computing, engineering simulation and applied machine learning for design and analysis.

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 .

Note for international and European applicants:details of how your qualification compares to the published academic entry requirements can be found on our Country Specific Entry Requirements page.

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

Eligibility criteria

UK & International students

EPSRC DLAstudentships are available to home and international students. Up to 30% of our cohort cancompriseinternational students. Once the limit has been reached, we are unable to make offers tointernational students.We are still accepting applications from international applicants.International students will not be charged the fee difference between the UK and international rates. Applicants should satisfy the UKRI eligibility requirements.

PhD: Applicants for PhD 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.

Additional Information

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

Selection process

Please see our website for more information.