Developing Novel Tools for the Analysis of Local Order Using Total Scattering Data (TScat) University of Sheffield in United Kingdom

This PhD project at the University of Sheffield focuses on developing novel tools for the analysis of local order in materials using total scattering data (TScat). Total scattering is an advanced X-ray and Neutron scattering technique that reveals atomic-scale structural information, crucial for understanding material properties such as radiation tolerance, electrical resistivity, and mechanical strength. Despite its importance, total scattering studies are limited in metallurgical systems due to the complexity of data processing and analysis, which requires expertise in multiple software suites and their interdependencies.

The aim of this project is to lower the barrier to entry by creating integrated analytical tools and workflows that streamline the use of diverse software packages. The student will develop new software solutions to guide users through the analytical process, facilitate information transfer between tools, and improve the quality of data analysis. The project also includes the collection of curated datasets and the application of machine learning techniques to enhance the analytical workflow, expanding the use of total scattering and unlocking new areas of materials exploration.

As part of the MOSAIC group, the student will collaborate with colleagues at the ISIS Neutron and Muon Source, Diamond Light Source, and the STFC Scientific Computing team. The position is embedded within the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute, offering a structured 4-year doctoral programme with technical and professional skills training. The student will join a national cohort working to advance the digital and data revolutions in materials science.

Applicants should have a background in Materials Science, Chemistry, Physics, or Computer Science, with skills in coding, data analysis, or software engineering. The position is fully funded, covering tuition fees and a stipend, and offers additional training and cohort activities. The application process involves submitting an online application via the University of Sheffield portal, selecting the relevant doctoral training course, and completing both a local application form and a standard questionnaire. The deadline for applications is March 3, 2026.

For general enquiries, contact [email protected]. For application-related queries, contact Rebecca Milner ([email protected]). For technical or scientific questions, reach out to the lead supervisor, Dr Lewis Owen ([email protected]).