This scholarship is funded by the EPSRC Doctoral Training Partnership (DTP).
Start date: October 2021
Subject areas: Inverse problems, Optimal design, Computational modelling, biomedical engineering, CFD
Project supervisors: Dr Sanjay Pant
Project description:
Mathematical models in biomedical engineering often have a large number of unknown parameters. These parameters must be estimated through clinical measurements acquired individually in each patient. However, it is not always clear which measurements are most informative about the parameters and which parameters are most relevant to answer a given clinical question. This project will develop a framework for this problem of ‘optimal design’ through recent developments in probabilistic analysis and information theory [1,2,3,4] with a particular focus on problems encountered in biomedical engineering. Particular applications will include electrophysiology, computational biomechanics, and computational haemodynamics. Furthermore, within the developed framework, this project will assess how, and if, virtual subjects (i.e. computational models) can be used to replace real subjects fully or partially in clinical trials.
[1] A. Aggarwal, D. Lombardi, S. Pant. An information-theoretic framework for optimal design: analysis of protocols for estimating soft tissue parameters in biaxial experiments. Axioms, 10(2), 79, 2021.
[2] S. Pant. Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems, Journal of The Royal Society Interface, 15:142, 20170871, 2018.
[3] D. Lombardi and S. Pant. A non-parametric k-nearest neighbour entropy estimator. Physical Review-E, 93:013310, 2016.
[4] S. Pant and D. Lombardi. An information-theoretic approach to assess practical identifiability of parametric dynamical systems. Mathematical Biosciences, 268:66–79, 2015.
Location: Zienkiewicz Centre for Computational Engineering, Bay Campus, Swansea University.
Available resources/facilities: Access to (i) computational labs (ii) computing hardware and software, and (iii) high performance computing facilities.
Eligibility
Candidates should hold a minimum of an upper second class (2:1) honours degree (or its equivalent) in engineering, mathematics or physics, or a master’s degree in a subject area related to the project.
A strong background in applied mathematics and/or statistics and probability theory is required.
Knowledge/experience of programming in Python and/or MATLAB is essential, and skills in C/C++ or Fortran are desirable.
An interest in physiology is desirable.
We would normally expect the academic and English Language requirements (IELTS 6.5 overall with 5.5+ in each component) to be met by point of application. For details on the University’s English Language entry requirements, please visit – http://www.swansea.ac.uk/admissions/english-language-requirements/
This scholarship is open to candidates of any nationality.
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