Machine Learning Driven Corrosion Modelling in Bio Feedstock Refining

The global shift toward renewable bio based fuels is creating exciting new scientific and engineering challenges. Compared with traditional crude oil, bio feedstocks behave very differently during processing, sometimes causing much faster corrosion of refinery equipment. Understanding these behaviours is essential if society is to move confidently toward low carbon fuels. This PhD studentship offers the opportunity to contribute directly to this challenge by developing modern data driven tools that help predict and manage corrosion in next generation bio refineries.<br /> <br /> The project brings together leading researchers from University of Leeds, Imperial College London, University College London, and the University of Illinois at Urbana Champaign, supported by industrial scientists at BP. Regular engagement with bp and the experimental team in Illinois will provide unique insight into industrial corrosion challenges and support the development of adaptive, data driven sampling strategies to accelerate experimental progress. <br /> <br /> This environment will provide you with a unique perspective on how data, modelling, experiments, and industrial needs come together in an emerging area of sustainable technology. The research will also contribute to the creation of high throughput approaches for assessing the corrosivity of bio refinery environments.<br />

<p>The research will begin with building a strong understanding of the data available, including visualising patterns, identifying key variables, and exploring the underlying chemistry and physics that influence corrosion. From there, you will progress through a structured modelling pathway:</p> <p><strong>Start with non physical structured models</strong><br /> You will first build and test purely data driven statistical and machine learning methods. These may include regression models, tree based approaches, and other flexible ML tools that uncover patterns without assuming underlying physical mechanisms.</p> <p><strong>Move toward hybrid and physics informed models</strong><br /> Building on initial insights, you will then develop models that incorporate physical and chemical principles, such as the influence of temperature, reaction pathways, chemical concentrations, and exposure time. These models aim to improve interpretability and scientific validity while maintaining predictive performance.</p> <p>This staged progression ensures that the modelling framework evolves from black box learning toward approaches that reflect real corrosion mechanisms and support trustworthy predictions.</p> <p>A particularly exciting part of the project will involve working with collaborators in Illinois to design new experiments based on your model predictions. Using approaches such as adaptive experimental design and active learning, you will help identify which measurements would be most informative to perform next, allowing the research team to gather data more efficiently and focus effort on the most important questions.</p> <p>As the project progresses, you will explore ways of creating models that balance predictive accuracy with interpretability, including techniques that help reveal which chemical and physical factors matter most. You will also assess model reliability using cross validation and uncertainty analysis, ensuring the final tools can support real decision making in industrial contexts.</p> <p>This PhD will provide extensive training and development opportunities. You will gain experience in data science, machine learning, corrosion science, and chemical engineering, as well as in working within interdisciplinary and international teams. You will have opportunities to present your work at conferences, publish research papers, and engage directly with industrial partners on challenges connected to the future of sustainable fuel production.</p> <p>Applicants should hold a undergraduate or master degree in a relevant discipline, such as engineering, materials science, chemistry, physics, computer science, applied mathematics, or a related field. Prior experience in programming such as using Python or MATLAB, machine learning, data analysis, or corrosion science is advantageous. Candidates should also demonstrate curiosity, strong analytical skills, and enthusiasm for working across disciplinary boundaries.</p>

<p>To apply for this project you will need to make a formal application for research degree study through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University website</a>. You will need to create a login ID with a username and PIN. </p> <p>•    For <strong>Application type</strong> please select <strong>Research Degrees, Research Postgraduate</strong>. <br /> •    The admission year for this project is <strong>202627 Academic Year</strong>. <br /> •    You will need to select your <strong>Planned Course of Study</strong> from a drop-down menu. For this project, scroll down and select <strong>PhD Mechanical Engineering Full-time</strong>. <br /> •    The project start date for this project is <strong>1 October 2026</strong>, please use this as your <strong>Proposed Start Date of Research</strong>. <br /> •    Please state clearly in the research information section that the research degree you wish to be considered for is <strong>Machine Learning Driven Corrosion Modelling in Bio Feedstock Refining</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.</p> <p><strong>Please state in the Finance section that you are applying for the School of Mechanical Engineering Studentship 2026/27 IMPACTBio.</strong></p> <p>More information on how to apply is available on our website here. You will be required to provide a personal statement which outlines your interest in the project you are applying for, why you have chosen it and how your skills map onto the requirements of the project. </p> <p>Applications will be considered after the closing date of Friday 26 June 2026. 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><strong>Please note that you must provide the following documents in support of your application by the closing date of Friday 26 June 2026:</strong></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>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements below.</p> <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 reading, writing, listening and speaking components 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 School of Mechanical Engineering Studentship, in support of the IMPACT Bio Research Grant, providing the award of full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £21,805 per year for 3.5 years. <br /> <br /> You will be responsible for paying the overtime fee in full in your writing up or overtime year, currently £340 in Session 202526, but the scholarship maintenance allowance will continue to be paid for up to 6 months in the final year of award.</p> <p>Please note that there is only 1 funded place available to UK applicants only.  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><strong>Eligibility Criteria </strong></p> <ul> <li>Applicants must be eligible to pay fees at the UK Fee rate.</li> </ul> <p><strong>Other Conditions </strong></p> <ul> <li>Candidates who have previously been awarded a PhD or are currently registered on a PhD are excluded from applying. Those who were previously studying for a PhD but did not complete may be considered.  </li> <li>Award must be taken up by 1 October 2026.</li> <li>Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this scholarship.</li> </ul> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website or our  <a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment">fee assessment page</a> for information regarding Fee Status for Non UK Nationals. </p>

<p>Individuals interested in this project are encouraged to contact Professor Richard Barker by email to <a href="mailto:R.J.Barker@leeds.ac.uk">R.J.Barker@leeds.ac.uk</a> for further information</p> <p>For questions related to the application process and eligibility, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p>