AI-Assisted Modelling of Flow and Compaction of Refractory Metal Powders

One fully funded PhD scholarship is available jointly in the School of Computer Science and the School of Chemical and Process Engineering at the University of Leeds in 2026/27. This scholarship is open to UK home fee applicants only and covers full tuition fees plus a UKRI-aligned maintenance stipend and a Research Training Support Grant. <br /> <br /> This fully funded PhD project offers an exciting opportunity to develop AI-driven computational models for predicting and optimising the flow and compaction behaviour of refractory metal powders. The project is jointly hosted by the School of Computer Science and the School of Chemical and Process Engineering, and is conducted in collaboration with Plansee SE, a world-leading manufacturer of refractory metal components based in Austria.<br /> <br /> The project applies state-of-the-art machine learning techniques to fundamental challenges in powder metallurgy, including AI-assisted calibration of Discrete Element Method simulations, convolutional neural network-based prediction of powder flowability from scanning electron microscopy images, surrogate modelling for high-density powder pressing, and Physics-Informed Neural Networks for compaction mechanics. Refractory metals such as Tungsten, Molybdenum, and Tantalum are indispensable for extreme-environment applications, including high-performance aerospace structures, high-temperature turbine systems, energy generation infrastructure, and medical implants. The project will produce validated AI models capable of predicting powder behaviour at significantly reduced computational cost compared to conventional numerical methods, while yielding transferable frameworks applicable to broader classes of particulate materials.

<p>This project sits at the intersection of artificial intelligence, powder mechanics, and materials science. Refractory metals such as Tungsten, Molybdenum, and Tantalum are critical to advanced manufacturing across high-performance engineering and medical sectors, yet the powder metallurgical processes used to produce components from these materials remain challenging to model accurately and efficiently. This PhD project addresses that gap by integrating machine learning with physics-based simulation, combining deep learning architectures, surrogate modelling, and physics-informed neural networks with established discrete element method simulations. The aim is to develop validated computational tools capable of predicting powder flow and compaction behaviour at significantly reduced cost compared to conventional numerical approaches, while remaining transferable to broader classes of particulate materials and manufacturing processes.</p> <p>Please visit the <a href="https://eps.leeds.ac.uk/computing-research-degrees">School of Computer Science</a> and the <a href="https://eps.leeds.ac.uk/chemical-engineering-research-degrees">School of Chemical & Process Engineering</a> websites to find out more about our current research activities.</p> <p><strong>Selection Process</strong></p> <ul> <li>All applications will be considered after the deadline. Only complete applications will be considered.</li> <li>The final list of awards are based on academic merit and no other factors such as financial hardship are taken into account.</li> <li>Applicants will be e-mailed within six weeks of the deadline to confirm the outcome.</li> <li>The University will publish the names of the successful applicants within the University and externally on the University website within eight weeks of the relevant scholarship closing date.</li> </ul>

<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's website</a>. You will need to create a login ID with a username and PIN. </p> <p>•    For <strong>‘Appli</strong><strong>cation type’</strong> please select <strong>‘Research Degrees – Research Postgraduate’</strong>. <br /> •    The <strong>admission year</strong> for this project is <strong>2026/27 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>EPSRC DTP – Engineering</strong>’. <br /> •    The <strong>project start date</strong> 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>AI-Assisted Modelling of Flow and Compaction of Refractory Metal Powders</strong> as well as <a href="https://eps.leeds.ac.uk/computing/staff/11422/dr-arash-rabbani"><strong>Dr Arash Rabbani</strong></a> as your proposed supervisor.</p> <p>More information on how to apply is available on our website <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">here</a>. You will be required to provide a personal statement which outlines your interest in the project, why you have chosen it and how your skills map onto the requirements of the project.</p> <p>Applications will be reviewed and assessed after the closing date of Tuesday 30 June 2026. We will assess applications continuously as we receive them. We welcome and strongly encourage any potential applicants to contact the supervisor(s) for an informal discussion, prior to applying, and recommend submitting your application early. </p> <p><strong>Please note that you must provide the following documents in support of your application by the closing date of Tuesday 30 June 2026 or at the point you submit your application:</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 in the School of Computer Science is an IELTS of 6.5 overall with at least 6.5 in writing and at least 6.0 in reading, 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.

<p>A highly competitive EPSRC Faculty Doctoral Training Partnership Award, in collaboration with Plansee, providing full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £21,805 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 Faculty Doctoral Training Partnership Award Competition and selection is based on academic merit.</p> <p>Please note that there is 1 funded place available.  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 Home (UK) rate.</li> </ul> <p>If you are unsure whether you are eligible for UK fees/funding, please visit 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 or our <a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment">fee assessment page</a> regarding fee status for Non-UK nationals.</p> <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>Awards must be taken up by 1 October 2026, or soon after that. </li> <li>Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this scholarship.</li> </ul>

<p>If you are interested in this project or have any questions, please do contact the project supervisor, Dr Arash Rabbani by emailing <a href="mailto:A.Rabbani@leeds.ac.uk">A.Rabbani@leeds.ac.uk</a>.</p> <p>For further information about your application, including how to apply, please contact PGR Admissions by emailing <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>.</p>