Postdoctoral Research Associate in AI-enhanced Spectroscopic X-ray Imaging for Real-time Metal Solidification

Location: Department of Materials, Begbroke Science Park, Mount, Yarnton, Kidlington OX5 1PF

About the role

We are seeking an outstanding candidate to work in the Process Dynamics group to contribute to advanced research in AI-driven X-ray imaging for metal alloy solidification.

The successful candidate will contribute to this emerging field of AI-driven X-ray experimental science through the development of novel X-ray spectroscopic imaging experimental modalities, the design and application of AI and machine learning models to interpret complex X-ray datasets, and the integration of experimental and computational insights to generate actionable knowledge that advances sustainable metal manufacturing. The post holder will closely collaborate with other scientists and technicians from the group and the other project partners.

This is a fixed-term post for one year, working full-time for 37.5 hours per week and will be based in the Department of Materials at its Begbroke Science Park site, 5 miles north of Oxford.

The Project

The post is funded by UKRI - Engineering and Physical Sciences Research Council (EPSRC) grant “Artificial Intelligence X-ray Imaging for Sustainable Metal Manufacturing (AIXISuMM)”.

The vision of AIXISuMM is that transformative and efficient technologies to manufacture high-grade recycled metal alloys from low-grade scrap sources can be delivered by uncovering the missing science to engineer the solidification microstructure to tolerate higher level of impurities, by leveraging the combined power of multi-modal X-ray imaging and in-line artificial intelligence (AI). The project also involves the Detector Development Group at the Science and Technology Facilities Council (STFC) and Loughborough University together with a group of industry partners.

About You

You will hold a completed doctorate, or be near completion, in Materials Science, Physics, or a relevant engineering or physical sciences discipline.

You will have proven experience in designing and conducting X-ray imaging experiments, ideally using spectroscopic techniques for the classification and identification of phases in metallic systems such as aluminium alloys or steels.

You will have demonstrated expertise in applying machine learning and computer vision techniques for the analysis of scientific imaging data, preferably using Python, and hands-on experience building bespoke laboratory setups for imaging experiments investigating alloy composition or relevant applications.

You will have a strong publication record and excellent written and verbal communication skills, with the ability to present complex scientific data to specialist and geenralist audiences.

How to apply

You will be required to upload your CV and a supporting statement as part of your online application. Your supporting statement should list each of the essential and desirable selection criteria, as listed in the job description, and explain how you meet each one. CVs alone will not be considered. Please do not attach any manuscripts, papers, transcripts, mark sheets or certificates as these will not be considered as part of your application.

Only applications received online by 12.00 midday (GMT) on Monday 9 March 2026 can be considered. Interviews are scheduled to take place at the Department of Materials after 30^th March and you must be available on this date, either by Teams, Zoom or in person.

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