Deep Learning for Inverse Scattering Problems

We are recruiting new Doctoral Researchers to our EPSRC funded Doctoral Training Partnership (DTP) PhD studentships starting 1 October 2023. Applications are invited for the project Deep Learning for Inverse Scattering Problems

Successful applicants will receive an annual stipend (bursary) of approximately £19,668, including inner London weighting, plus payment of their full-time home tuition fees for a period of 42 months (3.5 years).

You should be eligible for home (UK) tuition fees there are a very limited number (no more than three) of studentships available to overseas applicants, including EU nationals, who meet the academic entry criteria including English Language proficiency.

You will join the internationally recognised researchers in the Department of Mathematics

The Project

This research project is focused on inverse scattering. When an incident probing electromagnetic, acoustic or elastic field is launched into a medium that contains an object, that object will disturb the field and scatter it. This disturbance can be detected, and this project will explore the use of Deep Learning to use these detected signals to determine the shape of the object.

Please contact Dr Simon Shaw at simon.shaw@brunel.ac.uk for an informal discussion about the studentships.

Eligibility

Applicants will have or be expected to receive a first or upper-second class honours degree in an Engineering, Computer Science, Design, Mathematics, Physics or a similar discipline. A Postgraduate Masters degree is not required but may be an advantage.

Skills and Experience

Applicants will be required to demonstrate knowledge of elliptic partial differential (e.g. Helmholtz) equations, experience in scientific computing (e.g. matlab, python, C/C++), and experience in the configuration and use of off the shelf PDE and deep learning software.

You should be highly motivated, able to work independently as well as in a team, collaborate with others and be able to communicate effectively both verbally and in writing.

How to apply

There are two stages of the application:

1.Applicants must submit the pre-application form via the following link https://brunel.onlinesurveys.ac.uk/epsrc-dtp-23-24-pre-application-form-brunel-university-lon-3 by 16.00 on Friday 26th May 2023.

2.If you are shortlisted for the interview, you will be asked to email the following documentation in a single PDF file to cedps-studentships@brunel.ac.uk within 24hrs.

Your up-to-date CV;
Your Undergraduate degree certificate(s) and transcript(s) essential;
Your Postgraduate Masters degree certificate(s) and transcript(s) if applicable;
Your valid English Language qualification of IELTS 6.5 overall (minimum 6.0 in each section) or equivalent, if applicable;
Contact details for TWO referees, one of which can be an academic member of staff in the College.

Applicants should therefore ensure that they have all of this information in case they are shortlisted.

Interviews will take place in June 2023.

Meet the Supervisor(s)

Simon Shaw - Simon Shaw is a professor in the Department of Mathematics in the College of Engineering, Design and Physical Sciences, and belongs to the Applied and Numerical Analysis Research Group. He is also a member of the Structural Integrity theme of our Institute of Materials and Manufacturing, and of the Centre for Assessment of Structures and Materials under Extreme Conditions, and of the Centre for Mathematical and Statistical Modelling. Shaw was initially a craft mechanical engineering apprentice but (due to redundancy) left this to study for a mechanical engineering degree. After graduation he became an engineering designer of desktop dental X Ray processing machines, but later returned to higher education to re-train in computational mathematics. His research interests include computational simulation methods for partial differential Volterra equations and, in this and related fields, he has published over thirty research papers. He is currently involved in an interdisciplinary project that is researching the potential for using computational mathematics and machine learning as a noninvasive means of screening for coronary artery disease. Personal home page: http://people.brunel.ac.uk/~icsrsss

Related Research Group(s)

Applied and Numerical Analysis - Focused on the analysis of mathematical models of biological, chemical or physical processes.