-
academia and industry. Uncover and quantify critical degradation mechanisms to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through
-
to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through clear communication to diverse audiences, including industry stakeholders and
-
diverse and under-represented backgrounds and can offer flexible working arrangements. Funding Sponsored by EPSRC and three industrial partners, this studentship will cover: PhD fees; research equipment and
-
using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new