|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||From £17,668 Home fees (2023/24) included plus an annual stipend paid at the UKRI rate (award for 2022/23).|
|Placed On:||20th September 2023|
|Closes:||16th October 2023|
Project title: Efficient Deep Surrogate Models for Inverse Problems
Project contact: Dr Taysir Dyhoum
Home fees (2023/24) included plus an annual stipend paid at the UKRI rate (£17,668 for 2022/23).
Mode of study: Full time
Eligibility: Open to home & overseas students.
Eligible overseas students will need to make up the difference in tuition fees.
Closing date: 16 October 2023
Expected start: January 2024
Applying imaging techniques for industrial or biomedical applications frequently requires solving non-trivial inverse problems that need to be solved numerically. Such inverse problems are sought to reveal object properties which cannot be observed directly from the measurements. The inverse problems can be seen as an inversion of the forward or observation model. By the nature of the corresponding experimental setup, the inverse problems are ill-posed or severely ill-defined (i.e., The solution may not exist, or if exists it may be very sensitive to the observation errors). Even when an ill-posed inverse problem can be re-defined through regularisation it still poses challenges as resulting regularisation parameters need to be estimated to find a compromise between the robustness & fidelity of the solution.
Therefore efficient, accurate, stable & reliable approaches are required but these are difficult to obtain analytically. The added difficulty, after discretisation of the problem, is typically a very high dimensionality of the non-observable space, e.g., in medical image reconstruction, the dimensionality of the “hidden” space can easily exceed 1M. Hence the proposed project will integrate machine/deep learning into the existing mathematical/statistical approaches to speed up knowledge-based modelling – i.e. PDEs solvers.
Aims and objectives
The project will be set up in a generic context of solving inverse problems, with methods validation using simulated as well as established real MRI & CT, and Electrical Impedance Tomography EIT problems.
Specific requirements of the project
How to apply
Interested applicants should contact Dr Taysir Dyhoum for an informal discussion.
To apply you will need to complete the online application form for a full-time PhD in Computing and digital technology (or download the PGR application form), by clicking the 'Apply' button, above.
You should also complete the PGR thesis proposal (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest.
If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to mailto:PGRAdmissions@mmu.ac.uk.
Please quote the reference: SciEng-TD-2023-deep-surrogate
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