Port Talbot, UK, United Kingdom
Onsite
Fully Funded PhD Studentship: Physics-Informed Artificial Intelligence for Predicting Corrosion and Material Degradation in Critical Infrastructure
Institution: University of Wales Trinity Saint David
Industrial Partner: TWI Wales, Port Talbot
Location: Primarily based at TWI Wales, Port Talbot, with access to University of Wales Trinity Saint David facilities as required
Duration: 3 years
Funding: Fully funded with a £21,403 stipend in Year , increasing to £22,046 in year two and £22,707 in Year 3.
Start date: October 2026
UWTSD Supervisors: Dr Seena Joseph, Dr Ashley Pullen
TWI NSIRC Supervisor: Dr Kai Yang
Project Overview
UWTSD and NSIRC (TWI) invite applications for a 3 year industry based PhD studentship focused on the development of physics informed artificial intelligence methods for predicting corrosion and material degradation in critical infrastructure.
Corrosion and degradation present major challenges for the safe and efficient operation of pipelines, energy systems and other high-value engineering assets. These issues can reduce asset life, increase maintenance costs and create significant safety and reliability risks. Existing corrosion monitoring and prediction approaches, including inspection-based methods, statistical models and physics-based models, can be limited when dealing with complex, non-linear interactions between material properties, environmental conditions and degradation mechanisms.
This PhD project will investigate how Physics Informed Neural Networks, integrated with complementary machine learning techniques, can be used to improve the prediction of corrosion and material degradation. The work will combine data-driven learning with physical principles such as electrochemical kinetics, diffusion and thermodynamic behaviour to develop predictive models that are more accurate, interpretable and suitable for industrial application.
The student will be based primarily at TWI Wales in Port Talbot, working closely with industrial experts and gaining exposure to real world challenges. The student will also have access to the facilities, academic supervision and research environment of The University of Wales Trinity Saint David as required.
Research Aim
The aim of this PhD is to develop a hybrid modelling approach that integrates physics informed neural networks with machine learning techniques to predict corrosion and material degradation in pipeline and critical infrastructure applications.
The project will combine inspection data, environmental measurements and synthetic data with relevant physical laws to support improved corrosion detection, degradation prediction, predictive maintenance and lifecycle assessment.
About NSIRC
NSIRC is a state-of-the-art postgraduate engineering facility established and managed by structural integrity specialist TWI, working closely with, top UK and International Universities and a number of leading industrial partners. NSIRC aims to deliver cutting edge research and highly qualified personnel to its key industrial partners.
Funding and Eligibility
This is a 3 year fully funded PhD studentship covering tuition fees and annual stipend of £21,403 in Year 1, increasing to £22,046 in Year 2 and £22,707 in Year 3
How to Apply
Information on how to apply and eligibility criteria can be found here:
Postgraduate Research Applications | University of Wales Trinity Saint David
Application can be found here:
PhD Engineering - Swansea (October - Full Time)
Applicants should submit:
In the covering letter, applicants should explain their engineering background, their interest in AI or machine learning, and why they are interested in applying these methods to corrosion, degradation and critical infrastructure.
Informal Enquiries
For informal enquiries, please contact:
Dr Seena Joseph
Director of Studies
University of Wales Trinity Saint David
Email: seena.joseph@uwtsd.ac.uk
Dr Ashley Pullen
Supervisor
University of Wales Trinity Saint David
Email: a.l.pullen@uwtsd.ac.uk
and/or
Dr Kai Yang
Industry Supervisor
TWI
Email: kai.yang@twi.co.uk
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