Postdoctoral Research Assistant in Artificial Intelligence X-ray imaging for sustainable metal manufacturing

University of Oxford; Oxford; On-site; GBP 37,000 - 48,000; Full time

Job summary

University of Oxford invites a Postdoctoral Research Assistant to join the Process Dynamics group in the Department of Materials at Begbroke Science Park, north of Oxford. The role focuses on real-time in-situ X-ray imaging of liquid metal solidification and the development of multi-modal imaging methods.

You will work on steel solidification and microstructure analysis, collaborating with partners and contributing to AI-assisted imaging approaches under the EPSRC AIXISuMM project.

Qualifications

• PhD (or near completion) in materials science or a related field.
• Experience in X-ray imaging of solidification or related analyses.
• Hands-on experience building bespoke experimental rigs.
• Experience performing experiments in a synchrotron environment.
• Programming skills with Python for data analysis and CV techniques.

Responsibilities

• Design and undertake real-time in-situ X-ray imaging solidification experiments.
• Investigate dynamics of crystal formation during solidification.
• Post-solidification measurement of key microstructural features using microscopy and other techniques.
• Collaborate closely with scientists, technicians, and project partners.

Skills

X-ray imaging

Python programming

Computer vision

Experimental materials science

Synchrotron experiments

Education

PhD in materials science or related field

Tools

Python

X-ray imaging equipment

Job description

UK date and time: 06-November-2026 13:10

Postdoctoral Research Assistant in Artificial Intelligence X-ray imaging for sustainable metal manufacturing

Department of Materials

About this role

We are seeking an outstanding candidate to work in the Process Dynamics group on understanding how impurities and/or additions to liquid metal alloys can be used to control the final microstructure and defects.

The successful candidate will work on the design and undertaking of real-time in-situ X-ray imaging solidification experiments to investigate the dynamics of crystal formation both using laboratory and synchrotron sources.

The work will focus on the investigation of the solidification of steel and development of novel multi-modal imaging methodologies. Post-solidification measurement of key microstructural features using various microscopy and other techniques will also be performed.

The post holder will closely collaborate with other scientists and technicians from the group and the other project partners.

The post will be for 1.5 year and 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 overarching vision of AIXISuMM is to enable the development of transformative, efficient and sustainable manufacturing technologies for producing high-quality recycled aluminium alloys and steel from low-grade scrap materials. The project aims to achieve this by advancing the fundamental understanding of solidification processes and developing strategies to engineer solidification microstructures that can tolerate higher levels of impurities. This will be achieved by harnessing the combined capabilities of multimodal X-ray imaging and in-line artificial intelligence (AI) to uncover the underlying science and accelerate technological innovation. The project brings together a multidisciplinary team of researchers, including the Detector Development Group at the Science and Technology Facilities Council (STFC), Loughborough University, and a consortium of industrial partners.

About you

The appointed person will be an experimental materials scientist or process engineer with experience in X-ray imaging of solidification or related analytical approach and a doctorate (or be near completion) in materials science or a relevant engineering or physical sciences discipline.

Hands-on experience in developing and building bespoke experimental rigs and carrying out experiments in a synchrotron environment is required.

The appointed person will require programming skills and experience in computer vision techniques, preferably in Python, for the development of semi and fully automatic data analysis algorithms.