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

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 initially focus on the analysis of existing X-ray imaging data of the solidification of aluminium alloys and develop novel multi-modal imaging methodologies for the investigation of aluminium and steel solidification. 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 is fixed-term for two years and based in the Department of Materials at its Begbroke Science Park site, 5 miles north of Oxford. 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 preferred. 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. The post is funded by UKRI - Engineering and Physical Sciences Research Council (EPSRC) grant “Artificial Intelligence X-ray Imaging for Sustainable Metal Manufacturing (AIXISuMM)” and is fixed-term for up to 2 years. 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. All applications must be made online using the Oxford University E-Recruitment system, no later than 12 noon on 22 November 2024.