High-Throughput 3D/4D Microstructure Analysis and Feature Tracking for Alloy Design

About the Project

Supervisory Team: Dr Dikai Guan

This project will develop high-throughput program scripts for 3D/4D microstructure analysis and feature tracking to accelerate alloy design based on our developed Track-Rex toolbox (https://github.com/TrackRex/Track-Rex). Using advanced microstructure characterisation and deep learning, the PhD will create automated pipelines linking manufacturing, microstructure evolution, and materials performance, ultimately supporting impactful, interdisciplinary research.

Alloy design is critical for creating lighter, stronger, and more sustainable components for aerospace, transport, and energy systems. However, understanding and predicting the performance of advanced alloys requires analysing how their complex 3D and time-evolving (4D) microstructures form during manufacturing and change under service conditions. Modern characterisation techniques such as 3D Electron Backscatter Diffraction (3D EBSD), Lab Diffraction Contrast Tomography (LabDCT), and synchrotron X-ray tomography can capture unprecedented detail on grain growth, phase transformation, and defect evolution — but they generate terabyte-scale multimodal datasets that are challenging to reconstruct, segment, and interpret. This PhD will address this bottleneck by building on the group’s open-source Track-Rex toolbox (https://github.com/TrackRex/Track-Rex) to create high-throughput, AI-assisted pipelines for 3D/4D microstructure reconstruction, segmentation, and feature tracking. Using deep learning and graph-based algorithms, the project will automate tasks that currently require extensive manual effort, enabling researchers to connect processing–structure–property (PSP) relationships faster and more reliably. The student will work closely with industrial partners contributing representative additive manufacturing (AM) alloy datasets and case studies, ensuring real-world relevance. We will obtain multimodal datasets (3D EBSD, LabDCT, synchrotron tomography) from in-house printed representative AM samples, project partners, and open repositories (e.g., Zenodo). Data will capture crystallographic, morphological, and defect information across scales. The project offers access to state-of-the-art facilities at the University of Southampton, Henry Royce Institute, ESRF and Diamond Light Source, including advanced additive manufacturing labs, high-resolution microscopy, synchrotron partnerships, and the Iridis high-performance computing cluster.

Training will include hands-on advanced materials characterisation, and opportunities for collaboration across disciplines. Graduates will gain highly transferrable expertise at the intersection of materials science and artificial intelligence, preparing them for careers in research, industry, and technology development.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 31 May 2026. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships | Doctoral College | University of Southampton Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online:  Search for a Postgraduate Programme of Study (soton.ac.uk)

• Programme type: Research
• Academic year: 2026/27
• If you will be full time or part time
• Faculty: Engineering and Physical Sciences

Search for programme PhD Engineering & the Environment (7175)

Please add the name of the supervisor in section 2 of the application.

Applications should include:

• your CV (resumé)
• 2 academic references
• degree transcripts/ certificates to date
• English language qualification (if applicable)

For further information please contact: feps-pgr-apply@soton.ac.uk