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Mathematical modelling for clinical outcome prediction in kidney transplantation (NK22)

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Are you keen to undertake a cross-disciplinary project involving engineers, physicists, clinical researchers and transplant doctors? Interested in biomedical engineering or systems biology? This might be the project for you.

The project: This is a joint venture between the School of Engineering at the University of Warwick and the Kidney Transplantation Unit at the University Hospitals Coventry and Warwickshire. The project aims to develop novel mathematical approaches, based on modern Machine Learning algorithms, for prediction of acute kidney rejection and chronic graft loss after kidney transplantation in patients with end stage of renal failure. We would like to use mathematical modelling to identify the key factors associated with significant risk of kidney rejection and loss. The project will provide a unique opportunity for the PhD student to engage with the specialists from distinct research areas and gain working experience in highly cross-disciplinary environment. We are well-established team with joint publications, conference trips, research grants, and we meet on regular basis to exchange the research ideas and progress.

Funding Notes

This is a self-funded position.


Eligibility: UK or EU candidates with a 1st or 2.1 UK Honours degree in either of the subjects: Engineering, Mathematics, Physics, Computer Science, Systems Biology will find this studentship especially relevant. Applications from candidates with medical science background who are interested in predictive modelling are welcome too. Excellent data analysis and computing skills are essential (e.g. programming MATLAB/C/C++). We are looking for confident, self-motivated candidates with good interpersonal skills, and abilities to perform excellent research leading to high quality research outcomes and publications.

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