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Machine learning approaches to advance clinical decision making


Project Description

In this project we will use machine learning to process patient health records in order to support medical decision making. Multifaceted data will be combined and analysed to advance the standard of care.

Massive amounts of data related to long term health conditions is routinely collected by professionals. The analysis of these data sets is challenging not only because of the volume of the data, but especially because of its variety. Standard techniques fail at combing and exploit all information contained therein into one coherent framework that is amenable to statistical analysis.

In this project we will build on our previous research to devise such a framework. Approaches such as machine learning (artificial neural networks, support vector machines, decision trees, etc) and state space modelling will provide the basis for this demanding project.

Efficient visualisation and presentation of the results will be used to communicate the findings to medical professionals. Involvement of various stakeholders will guarantee a tailored solution maximising the benefit for patients.

Young researchers will be embedded in an active and striving environment, with state of the art facilities and strong connections to local stakeholders and industry.

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Applied Mathematics or equivalent.

• Apply for Degree of Doctor of Philosophy in Physics
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV
• Details of 2 academic referees

Informal inquiries can be made to Prof Bjoern Schelter () with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()

Funding Notes

This project is advertised in relation to the research areas of the discipline of Applied Mathematics, Data Analysis and Machine Learning. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THESE PROJECTS. Applicants should also be aware that Additional Research Costs of £5,000 are required (above Tuition Fees and Living Expenses) for advanced computer hardware and conference fees and travel.

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