Project title: X-ray imaging using ultrafast laser-generated soft X-rays and machine learning

Supervisory Team: W.S. Brocklesby, J.G. Frey (Chemistry)

Project description

A new area of imaging has recently been opened up which uses coherent light to illuminate objects, and computer algorithms to analyse the scattered light and generate images. Developments in ultrafast lasers have made sources of coherent soft X-rays possible in the lab, rather than relying on large-scale installations like synchrotrons. The combination of these two techniques has been successful in producing a new generation of X-ray microscopes, and at Southampton we have been at the forefront of this development. In particular, we have chosen areas of biology where imaging below the 100nm length scale can provide new information about processes occurring within cells.

Recently, the idea of combining algorithmic image reconstruction techniques with techniques based on machine learning has been proposed. Many of the problems of image reconstruction are similar to those addressed by machine learning (ML), particularly using convolutional neural networks. We aim in this project to use soft X-ray scattering and a combination of algorithmic and ML-based computer techniques to develop new ways of imaging, particularly in the area of biological science.

As part of the project you will become familiar with high-energy ultrafast laser science, using lasers with pulse lengths below 50 femtoseconds and peak powers in the terawatt regime, and you will also be trained in the use of algorithmic and ML techniques for data analysis and image reconstruction. Significant research expertise in both high-energy ultrafast lasers and machine learning exists in Southampton, and this project will involve working with members of the EPSRC-funded AI for Scientific Discovery network.

Entry Requirements

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

Closing date: Applications are accepted throughout the year and several start dates throughout the year are possible. Applications for the typical Sept./Oct. 2023 start should be received no later than 31 August 2023.

Funding: For UK students, Tuition Fees and a stipend of £20,000 tax-free per annum for up to 3.5 years.

How To Apply

Apply online: PhD Application | Research | University of Southampton. Select programme type (Research), 2023/24, Faculty of Physical Sciences and Engineering, next page select “PhD ORC (Full time)”. In Section 2 of the application form you should insert the name of the supervisorBill Brocklesby

Applications should include:

1. Curriculum Vitae
2. Two reference letters
3. Degree Transcripts/Certificates to date

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

The Zepler Institute is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.