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(A*STAR) EMLASCI: Experimental Machine Learning for Advanced Spectral and Correlative Imaging.

   Department of Materials

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  Dr T Burnett, Dr Amin Garbout, Mr Thomas Zillhardt, Dr Nicola Wadeson  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The project is an international interdisciplinary collaboration between researcher at University of Manchester and ARTC and SIMTech in Singapore. We would investigate the potential of machine learning to exploit the use of hyperspectral X-ray CT with a case study applied to the identification of different phases in metal alloys. Hyperspectral images acquired from different 2D/3D modalities (new 6x2 HEXITEC hyperspectral detector, EDX, EBSD, SIMS or XRF techniques…) will be correlated with X-ray CT images of Additively Manufactured parts and friction welded joints used in the aerospace and medical sector to build a multi-dimensional image dataset for model training.

In Collaboration with ARTC (A*STAR Singapore), application of ML in hyperspectral and tomographic imaging for waste management and remanufacturing will be investigated.


Applicants must have obtained or be about to obtain a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science, engineering or technology. 

Before you Apply

Applicants must make direct contact with preferred University of Manchester supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How To Apply

To be considered for this project you MUST submit a formal online application form - full details on eligibility how to apply can be found on our website. On the online application form select PhD Materials Programme. Please ensure you include the full project title in your application, i.e. (A*STAR) EMLASCI: Experimental Machine Learning for Advanced Spectral and Correlative Imaging.

Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. If you have any queries regarding making an application please contact our admissions team [Email Address Removed]

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Funding Notes

Funding will cover tuition fees and stipend only. This scheme is open to both UK and international applicants. However, we are only able to offer a limited number of studentships to applicants outside the UK. Therefore, full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme. Candidates will be required to split their time between Manchester and Singapore, as outlined on


1. Egan CK, Jacques SDM, Wilson MD, et al (2015) 3D chemical imaging in the laboratory by hyperspectral X-ray computed tomography. Sci Rep 5:15979.
2. Dudak J (2020) High-resolution X-ray imaging applications of hybrid-pixel photon counting detectors Timepix. Radiat Meas 137:106409.
3. Connolley T, Magdysyuk O V, Michalik S, et al (2020) An {\it operando} spatially resolved study of alkaline battery discharge using a novel hyperspectral detector and X-ray tomography. J Appl Crystallogr 53:1434–1443.
4. Egan CK, Jacques SDM, Cernik RJ (2013) Multivariate analysis of hyperspectral hard X-ray images. X-RAY Spectrom 42:151–157.
5. Matthew R. Carbone, Mehmet Topsakal, Deyu Lu, and Shinjae Yoo (2020) Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy Phys. Rev. Lett. 124, 156401 – Published 16 April 2020
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