Project background:
Polycrystals are the building blocks of numerous materials around us, from metals to ceramics, influencing their mechanical, thermal, and electrical properties. Understanding their intricate structures is crucial for advancing fields such as aerospace, engineering, and energy. Determining the intricate structure of the polycrystals is a laborious and restrictive task, and only a handful of measurement techniques are capable of fully imaging material microstructure. Laser ultrasonics is one of these techniques and it does this by linking the elasticity of individual crystals and measured ultrasound wave speeds. However due to this being an ill-defined problem, a computationally intense search is performed to find a correct solution of what is measured. By combining state-of-the-art laser ultrasonic imaging techniques with advanced artificial intelligence, the aim of this project will be to shortcut the current search process in classify crystallographic orientation. This will be built upon where machine learning algorithms will be developed to extract material elasticity information, phase and even composition all from a single set of laser ultrasound measurements.
Environment:
This exciting opportunity is based within the Optics and Photonics Research Group at the Faculty of Engineering (FoE), University of Nottingham which conducts cutting-edge research into the development of imaging technology for material characterisation. The Faculty of Engineering provides a thriving working environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE supports this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners.
Applicant:
The suitable candidate should demonstrate an enthusiasm for multidisciplinary research, with a minimum Master’s level degree classification of Merit and/or an undergraduate degree of UK 2:1 – however a Distinction and/or 1st class would be highly desirable. We welcome candidates from a broad range of disciplines, including Engineering, Physics, Mathematics, and Computer Science – candidates with experience with Machine Learning and/or Crystallography would be particularly suited for this project. This project will involve elements of both programming and experimentation, so applicants ideally should have an analytical and computational background, as well as the drive to learn how to operate laser ultrasonic inspection systems. In joining our team, we aim to help students develop their technical and non-technical skills, such as the ability to communicate effectively particularly with industry partners.
Funding and Support:
Fully funded studentship, which includes a minimum tax-free stipend of £19,237, are competitively available for UK home fee eligible students. Note that the funding associated with this role is awarded via an internal competition and is therefore only confirmed sometime after the admission application is approved.
Application:
