[School of Engineering PhD Scholarships] Sensing the Transition: Integrating AI, Simulation and Stakeholder Knowledge for Net-Zero Rare-Earth Value Chains

About the project

About the Project Rare-earth elements power the wind turbines, electric vehicles and digital technologies driving the UK's transition to net zero, yet their supply is highly concentrated in a small number of countries, and turning promising recycling technologies into viable businesses remains a major challenge. This interdisciplinary PhD project offers the opportunity to help address that challenge, combining artificial intelligence, simulation modelling and stakeholder research to understand how organisations can build resilient, circular rare-earth value chains. Working within the Department of Mechanical and Aerospace Engineering, you will develop and test a "sensing-to-decision" framework showing how businesses and policymakers detect and respond to signals, from new regulation to shifting markets, that shape the future of rare-earth supply. Drawing on Dynamic Capabilities Theory, you will conduct a systematic literature review and semi-structured interviews with industry and policy stakeholders to understand how information becomes strategic decisions. Using Python and AI techniques, you will integrate this qualitative insight with quantitative data to build simulation models testing scenarios for recycling, alternative sourcing and circular business models, validated through stakeholder workshops. In your final year, you will translate your findings into practical recommendations for UK industry and policymakers, aligned with the UK's Critical Minerals Strategy: Vision 2035. This is a timely, high-impact project: rare-earth elements sit at the heart of current UK industrial and net-zero policy, and your research will speak directly to live debates on supply chain resilience, circularity and economic security, with real potential to influence practice and policy beyond academia. You will be supervised by Dr Okechukwu Okorie, whose Royal Academy of Engineering Research Fellowship on circular economy modelling for net-zero manufacturing gives you access to established industry and policy networks and mentorship. You will receive structured training in systematic review methods, Python and AI/machine-learning techniques, simulation and scenario modelling, and qualitative research skills including interviewing and stakeholder engagement, alongside the University's Doctoral Academy programme in research integrity, project management and transferable professional skills, supported by regular supervision and annual progress review. We are looking for a highly motivated graduate holding, or expecting to achieve, at least a 2:1 undergraduate degree (or equivalent) in engineering, sustainability, data science, business or a related discipline; a relevant Master's qualification would be an advantage. You should have a genuine interest in interdisciplinary research spanning technical and organisational challenges. Programming experience, particularly in Python, and simulation modelling on hybrid models, is desirable but not essential, as full training will be provided. Strong written and verbal communication skills are important, since you will engage regularly with industry and policy stakeholders throughout the project. The University of Manchester's Department of Mechanical and Aerospace Engineering offers an excellent research environment, world-class facilities and a vibrant, supportive postgraduate research community within the Faculty of Science and Engineering. For further information, or to discuss the project informally, please contact Dr Okechukwu Okorie, Department of Mechanical and Aerospace Engineering, School of Engineering, Faculty of Science and Engineering, University of Manchester. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Engineering, Physics, Mathematics & Statistics, Computer Science, Material Science (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Engineering, Physics, Mathematics & Statistics, Computer Science, Material Science (or international equivalent). This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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