|Funding for:||UK Students, EU Students|
|Funding amount:||Please see details below|
|Placed On:||11th December 2018|
|Closes:||11th February 2019|
EPSRC CASE COMPETITION Studentship funded by EPSRC and Bayer AG. This includes UK/EU fees (£4,400 Session 2018/19 rate), maintenance (£17,777 Session 2018/19 rate) and funding for consumables for 3.5 years. UK applicants will be eligible for a full award paying tuition fees and maintenance. European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course.
Number of awards: 1
Computational Fluid Dynamics (CFD) has been used extensively in medicine to model the flow of blood in large arteries and veins and in the heart, but our understanding of the spatial propagation of body fluids on the microscopic level is more limited.
The purpose of this PhD project is to enable the study of these processes by developing multi-phase CFD models for flow, diffusion and permeability in healthy and diseased biological tissues. Model development and validation will be guided by in-silico modelling as well as 4D dynamic data obtained by Magnetic Resonance Imaging (MRI) and indicator-dilution experiments.
Specifically, the project will produce a set of spatiotemporal models for tissue flow in several organs and diseases (such as brain, muscle, liver and cancer) that provide an accurate fit to measured data. Numerical solutions will be provided for the forward problem of generating 4D data from a given flow fields and boundary conditions, and for the inverse problem of mapping the flow fields based on measured data.
A set of simplified digital tissues and organs will be coupled to models of the MRI scanner to provide a ground truth for the in-silico evaluation of these solutions under realistic experimental conditions. This entirely novel approach to analysing and interpreting 4D dynamic MRI data will not only significantly improve upon the accuracy of current 1D models but can also reveal new insights on the way nutrients are delivered to healthy and diseased biological tissue. Furthermore, this will increase our understanding of how disease affects the function and structure of organs, and may in time improve patient outcomes by better diagnosis and more personalised treatment planning.
In a final stage we will assess how application of these methods can improve survival of patients with liver cancer through better predictions of surgical risk. This will be achieved by exploring the clinical utility by developing a planning system for liver surgery that accounts for spatial patterns of tissue flow propagation.
For key benefits, see our project page.
Applications are invited from candidates with or expecting a UK first class honours degree, and/or a Master's degree in physics, or a relevant science/engineering degree such as (but not limited to) chemistry, biology, chemical engineering, electronic engineering, mechanical engineering, or mathematics. The candidates need to demonstrate their potential and ability to complete the proposed project successfully by providing professional achievements (e.g. previous research experience).
If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.
How to apply
For full details on how to apply, see our project page through the university's website.
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