[School of Engineering PhD Scholarships] Towards Circular AI: Critical Materials, E-waste and Circular Economy Strategies

About the project

About the Project Artificial intelligence (AI) is expanding rapidly, transforming sectors ranging from healthcare and manufacturing to transport, energy and scientific research. However, this expansion requires increasingly powerful computing infrastructure, including GPUs, AI accelerators, servers and data centres. The manufacture of this hardware relies on complex global supply chains and a range of critical raw materials, while rapid technological development and hardware replacement may contribute to increasing quantities of electronic waste. While considerable attention has focused on the energy consumption and carbon emissions of AI, much less is known about its material footprint and how circular economy strategies could reduce these impacts. This PhD will investigate how we can make the hardware and infrastructure supporting AI more circular and sustainable. It will quantify current and future material requirements and electronic waste associated with AI computing hardware, identify critical materials and environmental hotspots, and explore strategies to reduce these impacts. These could include extending equipment lifetimes, reuse and refurbishment, repurposing hardware for less computationally demanding applications, component recovery, improved recycling, circular procurement and take-back schemes. The project will use a combination of quantitative sustainability assessment methods, including material flow analysis (MFA), life cycle assessment (LCA), life cycle costing (LCC) and scenario modelling. The student will develop future scenarios exploring how different rates of AI adoption, technological development, hardware replacement and implementation of circular economy strategies could influence material demand, environmental impacts and e-waste generation. An important aspect will be understanding potential trade-offs—for example, when is it environmentally preferable to extend the lifetime of existing AI hardware rather than replace it with newer but more energy-efficient equipment? The research is expected to generate new evidence on the material and e-waste implications of AI, identify priority materials and components for circular interventions, and quantify the environmental and economic benefits of alternative circular strategies. Ultimately, the project will develop a roadmap for circular AI hardware and infrastructure, providing practical recommendations for technology companies, data-centre operators, policymakers and other stakeholders. The successful candidate will join an interdisciplinary research environment at the University of Manchester working across circular economy, life cycle sustainability assessment, sustainable infrastructure and digital technologies. The student will receive training in LCA, MFA, LCC, scenario modelling, uncertainty analysis and quantitative sustainability assessment, as well as opportunities to develop skills in stakeholder engagement, scientific communication and policy translation. The project offers an opportunity to work at the intersection of two rapidly developing fields (artificial intelligence and the circular economy) and contribute to understanding how the growth of AI can be made environmentally sustainable. 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 such as engineering, environmental science, sustainability, industrial ecology, computer science, data science, materials science, economics, digital infrastructure, electronics, data centres, circular economy and environmental assessment (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, environmental science, sustainability, industrial ecology, computer science, data science, materials science, economics, digital infrastructure, electronics, data centres, circular economy and environmental assessment (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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