Creating AI Models for the Automated Spectroscopic Characterisation of Materials

About the Project

This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute.

Automated materials characterisation enabled by artificial intelligence (AI) is becoming a reality, for example, global demonstrations in biology with Alphahold led to a Nobel Prize in 2024. Cole et al have recently delivered AI-based materials characterisation software for materials science using spectroscopy data via the automated classification of infra-red and NMR spectroscopy.[1,2] This PhD project will further these AI developments by delivering new AI models that automate materials characterisation from other forms of vibrational spectroscopy. Once ready, the AI models will be applied to an environmentally important case study in sustainable packaging. The new AI models will become part of the Royce Digital Materials Foundry that serves the UK materials community; see: https://www.royce.ac.uk/programmes/digital-materials-foundry/.

The PhD student will therefore have the opportunity to make a significant contribution to materials science and to global environmental sustainability in collaboration with industry; while also receiving state-of-the-art training in AI for materials science, programming, and core cohort-based training in transferable skills (programming, AI, digitalisation, research, leadership, communication) provided by this CDT scheme via the Henry Royce Institute (www.royce.ac.uk).

The PhD student will be housed at the Cavendish Laboratory at the University of Cambridge, within its brand new Ray Dolby Centre, a £303m award-winning building with state-of-the-art study facilities. See: https://www.phy.cam.ac.uk/news/the-ray-dolby-centre-wins-best-new-building-in-regional-award/

This PhD project will best suit a student with a degree in the physical or computing sciences who has a highly interdisciplinary aptitude, strong interest in python programming, artificial intelligence and machine-learning for energy-sustainable materials-science applications.

Funding Notes

This is a4-year PhD position at the University of Cambridge, fully funded via the UK’s EPSRC Materials 4.0 CDT, co-sponsored by the National Physical Laboratory.

The Materials 4.0 CDT is a UK doctoral training program focused on the digitalisation of materials science, combining AI, machine learning, and advanced materials research to accelerate innovation.

Enquiries

For general enquiries, please contact doctoral-training@royce.ac.uk.

For application-related queries, please contact Postgraduate Admissions at the University of Cambridge (Application enquiries). Please note that each partner of the CDT in Materials 4.0 will have its own application process.

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Professor Jacqui Cole (jmc61@cam.ac.uk)

Application Process

Please note that each partner of the CDT in Materials 4.0 will have its own application process.

The Materials 4.0 CDT is committed to Equality, Diversity and Inclusion. We strongly encourage applications from underrepresented groups.

Application Webpage

https://www.postgraduate.study.cam.ac.uk/courses/directory/pcmmpddnc

Click on 'Apply Now' and complete the form.

References

[1] G. Jung, S. G. Jung, J. M. Cole, “Automatic materials characterization from infrared spectra using convolutional neural networks”, Chem. Sci., 2023,14, 3600.
[2] S. Liu, J. M. Cole, “Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks”, J. Chem. Inf. Model. 2025, 65, 16, 8435.