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Fully funded PhD studentship in machine learning for clinical diagnosis.

School of Mathematical Sciences

Location:  Highfield Campus
Closing Date:  Friday 31 July 2020
Reference:  1236720PJ

Fully funded PhD studentship in machine learning for clinical diagnosis.

Applications are invited for a fully-funded PhD studentship in machine learning to support clinical decision-making. 

The aim of this project is to develop machine learning algorithms specifically tailored to learn clinical outcomes from the kinds of time-series and imaging datasets commonly collected in the clinic. 


We will specifically focus on two related problems in paediatric respiratory medicine: (1) diagnosis of obstructive sleep-disordered breathing from pulse oximetry time-series data; (2) prediction of cystic fibrosis progression from x-ray imaging data. However, the overarching aim of the project is to use these as examples to develop machine learning methods for time-series and imaging data that are applicable to numerous other diseases, with the potential to improve healthcare more widely. 

   

This is a challenging project and the successful candidate will have a first class undergraduate degree in Mathematics,  Computer Science, Physics or related field, with a  background and/or demonstrated interest in clinical applications of quantitative methods. 


This project will be supervised by Professor Ben MacArthur (Professor of Applied Mathematics, university of Southampton) and Dr Julian Legg (Head of the Paediatric Respiratory Department, University Hospitals Southampton NHS Foundation Trust).   


Enquiries should be made via email to Professor Ben MacArthur (bdm@soton.ac.uk) in the first instance. 


To apply for this funded project you will need to complete an online application. Details on how to apply can be found on our website here: http://www.soton.ac.uk/maths/postgraduate/research_degrees/apply.page and you can find general information regarding Postgraduate study at this University here: http://www.soton.ac.uk/postgraduate/index.shtml.

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