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Fully Funded PhD Studentship: Physics-Informed Artificial Intelligence for Predicting Corrosion and Material Degradation in Critical Infrastructure

Port Talbot
£22.7k/yr
Posted about 18 hours ago
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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 1, 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 behavior 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.

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Research Aim

The aim of this PhD is to develop a hybrid modeling 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

Applicants should submit:

  • A CV
  • A covering letter outlining their suitability for the project

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

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  • Dr Ashley Pullen

    • Supervisor
    • University of Wales Trinity Saint David
    • Email: a.l.pullen@uwtsd.ac.uk
  • Dr Kai Yang

    • Industry Supervisor
    • TWI
    • Email: kai.yang@twi.co.uk

TWI Culture

As one of the world’s leading independent research and technology organizations, we are committed to attracting, motivating, and retaining the best talent from around the world. Our goal is to develop the next generation of experts to address future industry challenges. We are committed to creating a culture that recognizes and respects the differences between people while valuing the contribution everyone makes to TWI. The diversity of our staff and students makes a positive and important contribution to our continuing success.

TWI offers a comprehensive training program, incorporating both in-house and external courses to support staff development.

TWI Values

  • Inclusion: Valuing the contribution from every individual, creating value for our customers
  • Teamwork: Building effective working relationships, we accomplish more together
  • Adaptability: Engaging positively with change to meet the needs of the business
  • Taking Responsibility: Achieving our objectives and personal development
  • Innovation & Expertise: Championing new ideas and sharing knowledge to solve industry problems
  • Customer Focus: Building trusting relationships with our customers

TWI Ltd is a world expert in engineering, materials, and joining technologies with significant property assets. We provide industry with advice and know-how in design, fabrication, failure analysis, and prevention. We offer opportunities to collaborate with inspiring and expert teams and a supportive environment in which you are actively encouraged to share your ideas and continually develop your own skills and knowledge.

If you are looking to join an organization that is fuelled by innovation, teamwork, and openness, this role could be for you.

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Skills

Physics-Informed Neural Networks
Machine Learning
Artificial Intelligence
Corrosion Prediction
Material Degradation Modeling
Electrochemical Kinetics
Thermodynamic Behaviour
Data-Driven Learning
Predictive Maintenance
Lifecycle Assessment
Engineering Analysis
Hybrid Modelling

Location

Port Talbot, Wales, United Kingdom

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