About the Project

This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute.

The studentship will develop machine learning methods to co-optimise environmental (life cycle assessment) and economic (return on investment) metrology to accelerate the circular economy for plastics and their multi-materials. While LCA & ROI are ofen used in materials innovation, laborious calculations prevent iteration. This studentship will develop AI-led multiparameter optimisations to apply these Materials 5.0 principles to both optimise waste fates (reuse, recycling, H2) for plastics and predict waste composition for plastic, glass and aluminium. With potential impacts for our industry partner, Resource Futures, and in shaping recycling investment and policy interventions from local and national government, this project has potential to reshape circularity of materials.

We seek an enthusiastic candidate who wants to join an interdisciplinary team in the Sustainable Materials Innovation Hub. The project would be suitable for a range of academic backgrounds in either machine learning and computer science, sustainability metrics and life cycle assessment, or materials engineering. Purpose-driven researchers keen to learn and collaborate on global challenges as change-makers will benefit from this unique opportunity within the Materials 4.0 CDT.

Funding Notes

This project is co-sponsored by Resource Futures, a data-driven waste management SME based in Bristol. Some travel to Resource Futures site may be required as part of the project.