Bayesian inference of pulsatile flow compliant boundaries

Education and Experience

Bachelor's Degree or equivalent (or expect to obtain by the start date) in fluid mechanics, applied mathematics, scientific computing, or related fields.

Applicants should have an excellent undergraduate or masters degree (or equivalent) in fluid mechanics, applied mathematics, scientific computing, or related fields.

Years of Research

Experience:

1 - 4 years.

• Some experience with programming, e.g. Python, C++, Matlab.
• Strong background in fluid mechanics, numerical methods, PDEs, Finite Element Methods, or functional analysis.

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 (SNR) 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 (CFD) simulation. This method uses Bayesian inference, which is 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.

Applicants must be in their first 4 years of their research career and have not yet been awarded a doctoral degree. The 4 years are counted from the date a degree was obtained which 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.

• Mathematics » Applied mathematics
• Engineering » Mechanical engineering

• English – Good