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Deep Learning for Inverse Scattering Problems

This research project is focused on objects that scatter fields. The field may be electromagnetic, acoustic or elastic. When an incident probing 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 one can hope to use the detected signal to identify properties of the scattering object. This project will explore the use of Deep Learning to use these signals to determine the shape of the object.

Applicants will be required to:

  • Demonstrate knowledge of elliptic partial differential (e.g. Helmholtz) equations.
  • Demonstrate experience in scientific computing (e.g. Matlab, Python, C/C++).
  • Configure and use off the shelf software.

 

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 similar discipline, and to meet English language requirements. A Postgraduate Masters degree is not required but may be an advantage.

Experience in Scientific Computing, Applied Mathematics and Partial Differential Equations is essential and experience in Python, Machine Learning, Unix and/or GNU/Linux is an advantage. You’ll also need a willingness to embrace a wide variety of both established and emerging areas in applied and computational maths, data science, artificial intelligence and machine learning. In addition, you should be highly motivated, able to work in a team, collaborate with others and have good communication skills.

Your application should address your experience and competencies to date in all of the areas mentioned above.

 

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s) for this Studentship


Simon Shaw - Simon Shaw is a reader in the Brunel Institute of Computational Mathematics (BICOM – please see http://www.brunel.ac.uk/bicom) and in the School of Information Systems Computing and Mathematics. He 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 as a noninvasive means of screening for coronary artery disease. Personal home page: http://people.brunel.ac.uk/~icsrsss

Stephen Langdon - I joined Brunel University London in October 2019, having previously worked at the University of Reading for over fifteen years, the last five as Head of the Department of Mathematics and Statistics. My research is in the area of Numerical Analysis, particularly the development, analysis and implementation of numerical methods for the solution of partial differential equations, and the application of such schemes to the solution of mathematical models arising from physical or biological processes such as acoustic or electromagnetic scattering, fluid flow, or tumour growth.