Qualification Type: | PhD |
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Location: | Southampton |
Funding for: | UK Students, EU Students, International Students |
Funding amount: | Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered. |
Hours: | Full Time |
Placed On: | 21st August 2024 |
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Closes: | 31st August 2024 |
PhD Supervisor: Dr Marcus Newton
Supervisory Team: Dr Marcus Newton, Dr Dan Porter, Prof Steve Collins, Prof Paul Quinn
Project description:
The University of Southampton is expanding its PhD research in the area of Quantum Technology Engineering. In addition to the research project outlined below you will receive substantial training in scientific, technical, and commercial skills.
Bragg coherent diffraction imaging (BCDI) is a lens-less far field x-ray imaging technique that allows three-dimensional (3D) imaging of quantum materials at the nanometre scale with a sensitivity below a single angstrom. To accomplish this, coherent x-rays from a synchrotron light source are used to illuminate a single nanocrystal which scatters to produce a diffraction (speckle) pattern. That pattern encodes all information about the arrangement of atoms within the nanocrystal. Iterative phase reconstruction computational methods are then routinely used to recover the complex three-dimensional electron density and phase information, which is related to strain in the nanocrystal.
Deep learning has emerged as a powerful alternative to the iterative phase retrieval approach, that can provide robust reconstruction of Fourier-space diffraction pattern data where iterative methods often fail to solve the phase retrieval problem. Although emphasis to date has focussed on inversion from Fourier-space to real-space images, the process of recovering real-space images remains unclear due to the inherent and currently intractable complexity of deep learning methods. In this project you will develop Physics-Aware Super-Resolution convolutional neural network tools to enhance the visibility of Fourier-space diffraction patterns thus enabling rapid and accurate reconstruction of phase information. You will build on our recent and significant developments in machine learning (ML) for phase retrieval.
This project is a collaboration with the Ada Lovelace Institute and Diamond Light Source.
If you are interested, please contact the supervisor for more information: Marcus Newton m.c.newton@soton.ac.uk
Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: 31 August 2024.
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: For UK students, tuition fees and a stipend at the UKRI rate tax-free per annum for up to 4 years rising annually. 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). Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Physics (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Dr Marcus Newton
Applications should include:
For further information please contact: feps-pgr-apply@soton.ac.uk
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