Machine-Learning Enhanced Corrosion Prediction for Net-Zero Energy Applications

This PhD project aims to develop advanced numerical models to predict internal corrosion in geothermal and carbon capture systems; two critical technologies in the global transition to low-carbon energy. These environments present unique challenges due to high temperatures, pressures, and complex chemical interactions, making accurate corrosion prediction essential for safe and cost-effective operation.<br /> <br /> The project will combine cutting-edge numerical simulation techniques with machine-learning to create predictive models that are not only scientifically robust but also easily accessible and implementable by industry. By integrating Artificial Intelligence, the models will be adaptable to real-world data and operational conditions, enabling proactive maintenance and reducing downtime.<br /> <br /> This work is of high importance to industry, where corrosion-related failures can lead to significant financial losses and safety risks. The ability to predict and mitigate corrosion will enhance asset integrity, extend equipment life, and support the deployment of sustainable energy technologies.<br /> <br /> The supervisory team brings extensive experience in numerical modelling and industrial collaboration, offering strong support and a clear pathway to commercialisation opportunities through model development. This project provides an exciting opportunity to contribute to impactful research with real-world applications and to work at the intersection of engineering, data science, and sustainability.<br /> <br /> An inclusive environment and supportive application process: As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education.<br /> <br /> Interested? Discuss this PhD Opportunity with Prof. Richard Barker by contacting: R.J.Barker@leeds.ac.uk<br />

<p style="margin-top:7px; margin-bottom:13px"><strong>Background:</strong></p> <p>This project is part of a broader multidisciplinary research initiative focused on improving the reliability and sustainability of energy infrastructure where we are developing predictive models for internal corrosion in geothermal and carbon capture systems, which are increasingly vital in the global shift toward low-carbon technologies.</p> <p>Our approach combines experimental research with advanced numerical modelling, allowing us to simulate a wide range of operational scenarios that are difficult or impractical to replicate in laboratory settings. This enables us to explore how various factors (such as fluid composition, material properties and system geometry) affect corrosion rates and mechanisms.</p> <p>To enhance accessibility and industrial uptake, we have been integrating machine-learning into the modelling framework. This allows for rapid adaptation to real-world data and to simplify implementation across industry. The supervisory team has extensive experience in numerical simulation and translating research into industry-ready tools, offering strong potential for real-world impact.</p> <p><strong>Research objectives:</strong></p> <p>In this PhD project, you will aim to develop predictive models for internal corrosion in geothermal and carbon capture systems, combining numerical simulation with machine-learning to create tools that are scientifically accurate and accessible by industry.</p> <p>Specific objectives include:</p> <ul> <li>Development of numerical modelling techniques (using COMSOL Multiphysics) to simulate corrosion processes under geothermal and carbon capture conditions.</li> <li>Application of machine-learning algorithms to numerical models and their datasets to enhance model adaptability and enable prediction based on operational data.</li> <li>Validation of corrosion models using experimental data from industrial/laboratory systems.</li> <li>Creation of user-friendly interfaces or toolkits that allow industry to implement the models easily within their existing workflows.</li> <li>Identification of key operational and environmental variables that influence corrosion rates, using sensitivity analysis and data-driven approaches.</li> <li>Exploration of commercialisation pathways, including collaboration with industry stakeholders to pilot and refine the predictive tools.</li> </ul> <p><strong>Training and Career Development</strong></p> <p>This PhD provides the opportunity to work at the intersection of engineering, materials science, and data science, combining numerical modelling, corrosion science, and machine-learning. As part of the project, you will work directly with industrial partners, gaining insight into real-world challenges and contributing to solutions with relevance. As a PhD researcher, you will benefit from the supervisory team’s strong track record in translating research into industry-ready tools, with opportunities to contribute to software development, and potential technology licensing.</p> <p>The supervisory team will provide hands-on training in computational modelling (e.g., finite element methods, Multiphysics simulation), machine learning techniques, and experimental validation methods (if the latter is of interest to the researcher).</p> <p>The supervisory team have a dedicated budget to support your travel to international conferences and are committed to enabling you to publish in high-impact journals, thereby building a strong professional network across academia and industry to support your career growth. The supervisory team will also provide the opportunity for you to shape the direction of your research based on your interests, whether that’s deepening the modelling framework, expanding the machine learning component, focusing on deployment and usability, or even exploring experimental validation in the laboratory.</p> <p><strong>Skills Required</strong></p> <p>We are seeking a candidate with a strong interest in numerical modelling and a background in engineering, physical sciences, or a related discipline. While prior experience with machine learning is advantageous, it is not essential, as training and support will be provided throughout the project. Similarly, a foundational understanding of electrochemistry or corrosion science is not a requirement at the outset. The supervisory team will offer structured training in these areas, ensuring the candidate develops the necessary expertise to succeed in this multidisciplinary project.</p> <p><strong>The Research Environment</strong></p> <p><strong>Why Study for a PhD at the School of Mechanical Engineering, University of Leeds?</strong></p> <p>The School of Mechanical Engineering at Leeds is a globally recognised centre of excellence, offering PhD students the opportunity to contribute to impactful research across diverse and cutting-edge fields. With 96% of research rated as “world-leading” or “internationally excellent” (REF 2021), the School is home to four specialist research institutes:</p> <ul> <li><strong>Institute of Medical and Biological Engineering</strong> – Pioneering innovations in joint replacement and regenerative medicine, with research that has influenced international clinical standards and improved outcomes for over a million patients </li> <li><strong>Institute of Thermofluids</strong> – Tackling global challenges in energy, transport, and sustainability through advanced fluid dynamics and heat transfer research</li> <li><strong>Institute of Functional Surfaces</strong> – Leading work in tribology, corrosion, and surface engineering, with applications in energy, carbon abatement, aerospace, automotive, and biomedical sectors</li> <li><strong>Institute of Design, Robotics and Manufacturing (iDRaM)</strong> – iDRaM brings together over 100 researchers and technical staff to tackle complex challenges in design, robotics, and advanced manufacturing. The institute is renowned for achieving real-world impact in applications spanning healthcare, aerospace, and industrial automation.</li> </ul> <p>PhD students benefit from access to state-of-the-art facilities, including UK-leading labs and simulation platforms, and are supported by the Leeds Doctoral College, which provides tailored training, wellbeing services, and career development opportunities.</p> <p>The School maintains strong industry links with organisations such as GE Global Research, Johnson & Johnson, Honeywell,  AECOM, Infineum and Total Energies, offering students opportunities to engage in collaborative projects, attend industry seminars, and build networks that support future careers in academia or industry </p> <p>Join a vibrant, international research community in a dynamic city, and be part of a university committed to solving real-world problems through engineering innovation.</p> <p><strong>The Institute of Functional Surfaces (iFS)</strong></p> <p>The Institute of Functional Surfaces (iFS) at the University of Leeds is a world-leading centre for research into the science and engineering of surfaces and interfaces. Surfaces play a critical role in determining the performance and reliability of engineering systems, from energy and transport to healthcare and manufacturing. iFS focuses on three core areas: tribology, surface engineering, and corrosion and flow assurance, addressing challenges that span from the nanoscale to large-scale industrial applications.</p> <p>PhD students at iFS work on cutting-edge projects on materials, surfaces and interfaces, addressing critical   global challenges  related to  climate change, sustainable energy, and advanced healthcare technologies. Research includes environmentally friendly lubrication solutions and  low-friction coatings for energy efficiency, corrosion mitigation solutions for energy and carbon abatement systems, and innovative surface treatments for biomedical implants. With access to world-class facilities through the University Bragg Centre and strong collaborations with industry, students gain hands-on experience and real-world impact.</p> <p>Joining iFS means becoming part of a vibrant, interdisciplinary community committed to advancing technology for a sustainable and resilient future, while building expertise highly valued across academia and industry.</p> <p><strong>Our Commitment to an Inclusive, Equitable and Diverse Research Community:</strong> As an international research-intensive university, we welcome students from all walks of life and from across the world. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</p> <p>We can help support your application! – Contact our <a href="https://contextualoutreach.leeds.ac.uk/pgr-diversity/access-to-research/">Access to Research Team</a></p> <div> <div> <div class="msocomtxt" id="_com_1" language="JavaScript"> <p class="MsoCommentText" style="margin-top:7px; margin-bottom:13px"> </p> </div> </div> </div> <div> </div>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <strong><em>EPSRC DTP Engineering & Physical Science</em>s </strong>and in the research information section that the research degree you wish to be considered for is <strong><em>Machine-Learning Enhanced Corrosion Prediction for Net-Zero Energy Applications</em> </strong>as well as <a href="https://eps.leeds.ac.uk/mechanical-engineering/staff/501/professor-richard-barker">Professor Richard Barker</a> as your proposed supervisor.  <em><strong>Please state in the Finance section that you are applying for the EPSRC Doctoral Landscape Award 2026/27: Mechanical Engineering.</strong></em></p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p>Applications will be considered after the closing date. Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application.  We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of Friday 14 November 2025:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date including grading scheme</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p>A highly competitive EPSRC Doctoral Landscape Award providing full academic fees, together with a tax-free maintenance grant at the standard UKRI rate (£20,780 in academic session 2025/26) for 3.5 years.  Training and support will also be provided.</p> <p>This opportunity is open to UK applicants only.  All candidates will be placed into the EPSRC Doctoral Landscape Award Competition and selection is based on academic merit.</p> <p>Please note that there is only 2 funded place(s) available and there are 17 projects in competition for this funding.  If you are successful in securing an academic offer for PhD study, this does not mean that you have been successful in securing an offer of funding.</p> <p>Please refer to the <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ukcisa.org.uk%2F&data=05%7C02%7CJ.S.Hewer%40leeds.ac.uk%7C07632c93c06a442dca3d08ddfc172939%7Cbdeaeda8c81d45ce863e5232a535b7cb%7C0%7C0%7C638943898649349324%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=nylGSov8jOc7hr6X%2FmfnfQPecbVUnGqgoSqVgPGy5K0%3D&reserved=0">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals</p>

<p>For further information about this project, please contact Professor Richard Barker by email to <a href="mailto:R.J.Barker@leeds.ac.uk">R.J.Barker@leeds.ac.uk</a></p> <p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p>