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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.

Entry Requirements:

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 a related subject area.

Many of our students have also undertaken a master's degree, although this is not compulsory.

International applicant eligibility requirements: Some restrictions apply to applicants from certain Asian countries. In general, students from Europe, the Americas, Africa, Australia, New Zealand, Korea and Japan are eligible to apply for the programme. Unfortunately, we cannot accept applications from south-east Asian countries such as Singapore, China and Malaysia.

International applicants must ensure they meet the academic eligibility criteria (including English language) as outlined before contacting potential supervisors to express an interest in their project.

How to Apply

To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the A*STAR PhD website https://www.bmh.manchester.ac.uk/study/research/astar/

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. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/


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 www.manchester.ac.uk/singaporeastar.

References

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. https://doi.org/10.1038/srep15979
2. Dudak J (2020) High-resolution X-ray imaging applications of hybrid-pixel photon counting detectors Timepix. Radiat Meas 137:106409. https://doi.org/https://doi.org/10.1016/j.radmeas.2020.106409
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. https://doi.org/10.1107/S1600576720012078
4. Egan CK, Jacques SDM, Cernik RJ (2013) Multivariate analysis of hyperspectral hard X-ray images. X-RAY Spectrom 42:151–157. https://doi.org/10.1002/xrs.2448
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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