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Uncertainty quantification for random moving boundary problems

Project Description

Moving boundary problems form an important class of mathematical models describing various processes with evolving interfaces from physics, engineering, biology, and chemistry. In real world applications such processes are affected by a variety of uncertainties which require modelling via random moving boundary problems. Being able to efficiently quantify such uncertainties is crucial for the mentioned applications. The objectives of this PhD project include (i) to construct, justify, analyse and test efficient algorithms for the Bayesian inverse problem within the moving boundary setting; (ii) use the algorithms for quantifying uncertainties in one of the main manufacturing processes for producing advanced composites - resin transfer moulding (RTM); and (iii) apply the algorithm to real data from composite laboratory experiments. The project will also involve questions related to design of experiments, e.g. optimal spatial positions and frequency for data collection under a restriction of the total number of data collection points.
To have more information about the topic, see the recent paper

Funding Notes

Summary: UK/EU students - Tuition Fees will be paid, and a full stipend provided at the RCUK rate (£15,009 per annum for 2019/20). There will also be some funds available to support conference attendance. The scholarship length will be 3.5 years.
Eligibility/Entry Requirements: We require an enthusiastic graduate with a 1st class degree in Mathematics, preferably at MMath/MSc level (in exceptional circumstances a 2:1 class degree, or equivalent, can be considered).
We are expecting that the successful applicant has a background in PDEs, Probability and Statistics and has exceptional computational skills.


For any enquiries please email to [email protected] or [email protected] or [email protected]

Related Subjects

How good is research at University of Nottingham in Mathematical Sciences?

FTE Category A staff submitted: 54.85

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