PhD Studentship: Machine learning for cardiac digital twins

at University of Nottingham
Published February 26, 2023
Location Nottingham, United Kingdom
Category Machine Learning  
Job Type Scholarship  

Description

Mathematical Sciences

Location:  UK Other Closing Date:  Tuesday 21 March 2023 Reference:  SCI2166

Closing Date: March 21st 2023

Project title: Machine learning for cardiac digital twins

Digital twins are virtual representations of physical objects, that are starting to be widely used in industry and healthcare. The twin can be used to track the health of the object, combining complex models and data. In this project, you will look at develop machine learning methodology to develop digital twins of the hearts of patients being treated for cardiac problems. In particular, looking at how to combine data that lives on manifolds, with patient specific simulations of their heart function. You will be joining a large team of researchers working on the problem, spread across Nottingham, Imperial College London, and Sheffield. As part of the project, you will be expected to collaborate with others in the team (mostly non-mathematicians), which will provide opportunities for developing a wide and valuable skillset.

Funding covers a stipend at the RCUK rate (£17,668 for 2022-23) and fees at the level of a UK domestic student.

A 3.5 year PhD studentship, beginning in October 2023.

Entry Requirements: Applicants are normally expected to have a 2:1 Bachelor or Masters degree or international equivalent, in a related discipline. Any offer will be subject to the University admissions requirements.      First class BSc (in Mathematics, Statistics, Computer Science, Physics or related quantitative subject) or an MSc in a relevant field.  Any offer will be subject to the University admissions requirements.

Application Process: Applications to be made via the central University of Nottingham admission process (NottinghamHub): https://www.nottingham.ac.uk/pgstudy/how-to-apply/how-to-apply.aspx

Enquiries to be directed to Dr Yordan P. Raykov: yordan.raykov@nottingham.ac.uk