Overview
Flow‑MRI (magnetic resonance imaging) is a non‑invasive imaging method that visualizes fluid flows in the body in 4D (3 spatial and 1 time dimension) without using ionizing radiation. It holds great promise for comprehensive characterization of blood velocity, particularly in the heart and major blood vessels, but is currently hindered by low signal‑to‑noise ratio and low spatial resolution.
The Principal Investigator’s research group has developed a method that assimilates sparse and noisy Flow‑MRI data directly into a computational fluid dynamics simulation. This method uses Bayesian inference, also known as probabilistic machine learning. The Bayesian inference code wraps around a differentiable finite element method code, which combines adjoint methods with Laplace’s method to assimilate data and estimate uncertainties.
Responsibilities
Extend Bayesian inference of Flow‑MRI data to 4D pulsatile flows within compliant boundaries; implement, test, and validate the results with data from compliant test objects in MRI scanners; increase image resolution and predictive accuracy of derived information such as pressure gradients and wall shear stress; assess the clinical relevance of this information by working with clinicians.
Qualifications
Applicants must be in their first four years of research career and have not yet been awarded a doctoral degree. These four years are counted from the date a degree was obtained that formally entitles one to embark on a doctorate.
According to the international mobility rules of the MSCA‑DN program, candidates must not have spent more than 12 months in the hosting country (UK) during the 36 months preceding the start of the PhD.
Applicants should have an excellent undergraduate or masters degree (or equivalent) in fluid mechanics, applied mathematics, scientific computing, or related fields.
Applicants should have experience with programming (e.g., Python, C++, Matlab) and the candidate will have a strong background in fluid mechanics, numerical methods, PDEs, finite element methods, or functional analysis.
EEO Statement
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
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