Data-Driven Alloy Design and Autonomous Alloy Development

About the Project

Combinatorial and autonomous approach for new material discovery with the combined modelling, machine learning, automation, and high throughput experimentation is the cornerstone of modern materials science. This approach has been approved to be highly successfully with the discovery and optimization of several materials systems. In this PhD project, you will be working on a data-driven based combinatorial approach for alloy discovery, and an autonomous robotic system for alloy fabrication, focusing on precious metal alloys for jewellery applications first, then branch it into alloys for aerospace and nuclear applications.

We are seeking a highly motivated candidate with a background in materials or mechanical engineering, with an interest in robotic systems for materials fabrication. You will join the Sustainable Physical Metallurgy Group in a collaborative environment, under the supervision of Prof Biao Cai, based in the School of Metallurgy and Materials at the University of Birmingham. You will work closely with our industry partner Cooksongold, part of prosperity partnership jointly funded by EPSRC and Cooksongold.

Funding Notes

A 3.5-year PhD studentship is available in the sustainable physical metallurgy group of Prof. Biao Cai within the School of Metallurgy and Materials at the University of Birmingham, with a stipend of at least £20,780 per year.