Marie Sklodowska-Curie Early Stage Researchers- Bayesian inference of pulsatile flow through compliant boundaries
CompanyUniversity of Cambridge
Location
Cambridge, England, United Kingdom
Employment TypeFull-time
Posted At6/1/2026
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Occupation TypeBusiness and related research professionals
Occupation Code Skill LevelHigher Skilled
Sponsorship Salary Threshold
£41,700
(£21.38 per hour)
Standard minimum applies
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Description
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.
The objectives of the proposed study are to (i) extend Bayesian inference of Flow-MRI data to 4D pulsatile flows within compliant boundaries; (ii) implement, test, and validate the results with data from compliant test objects in MRI scanners; (iii) increase the image resolution and the predictive accuracy of derived information such as pressure gradients and wall shear stress, and (iv) assess the clinical relevance of this information by working with clinicians.
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, the candidates must not have spent more than 12 months in the hosting country (UK), during the 36 months preceding the starting of the PhD.
Applicants should have (or expect to obtain by the start date) an excellent undergraduate or masters degree (or equivalent) in fluid mechanics, applied mathematics, scientific computing, or related fields.
The applicant will have some experience with programming, e.g. with python, C++, Matlab. The role holder will have a strong background in fluid mechanics, numerical methods, PDEs, Finite Element Methods, or functional analysis.
To apply for this studentship, please send your two page CV and transcripts if available to Prof. Matthew Juniper to arrive no later than 22nd July.
Please note that any offer of funding will be conditional on securing a place as a PhD student.
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.
The objectives of the proposed study are to (i) extend Bayesian inference of Flow-MRI data to 4D pulsatile flows within compliant boundaries; (ii) implement, test, and validate the results with data from compliant test objects in MRI scanners; (iii) increase the image resolution and the predictive accuracy of derived information such as pressure gradients and wall shear stress, and (iv) assess the clinical relevance of this information by working with clinicians.
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, the candidates must not have spent more than 12 months in the hosting country (UK), during the 36 months preceding the starting of the PhD.
Applicants should have (or expect to obtain by the start date) an excellent undergraduate or masters degree (or equivalent) in fluid mechanics, applied mathematics, scientific computing, or related fields.
The applicant will have some experience with programming, e.g. with python, C++, Matlab. The role holder will have a strong background in fluid mechanics, numerical methods, PDEs, Finite Element Methods, or functional analysis.
To apply for this studentship, please send your two page CV and transcripts if available to Prof. Matthew Juniper to arrive no later than 22nd July.
Please note that any offer of funding will be conditional on securing a place as a PhD student.
Candidates will need to apply separately for admission through the University's Graduate Admissions application portal; this can be done before or after applying for this funding opportunity. The applicant portal can be accessed via: www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg. The final deadline for PhD applications is 30th July 2026, although it is advisable to apply earlier than this.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.