PhD Studentship - Digital Twins for Personalised Cardiovascular Healthcare

Supervisors: Dr. Xu Xu, Professor Ian Halliday, Professor Richard Clayton, Harry Saxton

Cardiovascular diseases (CVD) are the leading cause of death worldwide. Invasive and non-invasive treatments carry patient risk and can fail to achieve the desired clinical outcome, due to the physiological state of a patient, who would therefore benefit from quantitative evaluations of risk and return.

What if quantitative assessment of e.g. heart interventions could be performed in silico, with the patient on the table? Digital twins are computational models that capture the structure and function of a real-world artefact and are updated regularly to both represent its current state and predict future behaviour. These ideas have been applied to assets such as bridges and wind turbines and are just beginning to be applied to patients. Let’s make digital twins of CVD patients: (i) real time and (ii) able to prognose (as well as diagnose)!

We will investigate every stage of digital twin construction, particularly their uncertainty quantification and parameter identification studies: specifically

1. Global sensitivity analysis, to determine the effects of input parameters upon clinically significant model outputs.
2. Surrogate modelling to reduce the computational cost, using e.g. Gaussian process emulation, to reduce the complexity of the original expensive model while restricting the loss of accuracy.
3. Robust parameter identification and state estimation from noisy measured data of patients, using e.g. unscented Kalman filter. 

We have already made methodological advancements through another PhD project. Here we offer the opportunity to extend our recent discoveries to (i) more complex (detailed) circulation models, with (ii) additional practical considerations of e.g the clinical pathway. The successful digital twins will eventually furnish clinicians with augmented data for target applications of CVD diagnosis and interventions.

About the school

99% of our research is rated in the highest two categories in the REF 2021, meaning it is classed as world-leading or internationally excellent. We are rated as 8th nationally for the quality of our research environment, showing that the School of Computer Science is a vibrant and progressive place to undertake research.

Candidate requirements

• Minimum 2.1 undergraduate honours degree or Master's degree with Merit in a relevant discipline (such as Computer Science, Mathematics or others related to the PhD topic).
• Excellent mathematical & programming background, with a keen interest in computational healthcare. Prior knowledge of statistical or numerical analysis would be an advantage.
• If English is not your first language, you must have an IELTS score of 6.5 overall, with no less than 6.0 in each component.

How to apply

To apply for a PhD studentship, applications must be made directly to the University of Sheffield using the Postgraduate Online Application Form. Make sure you name Dr. Xu Xu (Computer Science) as your proposed supervisor.

Information on what documents is required and a link to the application form can be found here - www.sheffield.ac.uk/postgraduate/phd/apply/applying

Funding notes

This PhD studentship will cover standard UK home tuition fees and provide a tax-free stipend at the standard UK Research Council rate (currently £19,237 for the 2024/25 academic year) for 3.5 years. If you are an overseas student, you are eligible to apply but you must have the means to pay the difference between the UK and overseas tuition fees by securing additional funding or self-funding. Further information on international fees can be found here: www.sheffield.ac.uk/new-students/tuition-fees/fees-lookup.

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