FairCFD

This is much more than just a PhD position!

Within the FairCFD Doctoral Network, you will benefit from a unique three-fold experience:

–  Contribute to technological innovation in the health industry by developing advanced and efficient CFD strategies.

–  Take part in a network-wide interdisciplinary effort to define and promote numerical sustainability in scientific research.

–  Join a vibrant network of 15 doctoral candidates, across 9 European countries, with access to cutting-edge network events, high-level training to technical and transverse skills, and secondments in both academic and industrial environments.

 See our website for more details on the network’s philosophy and actions, along with description of the other PhD positions (https://www.imft.fr/faircfd/project-presentation/).

Scientific background

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. Scans typically take 30 to 90 minutes because low SNR images must be averaged to generate high SNR images that can be interpreted. Even then, processed images remain noisy, particularly at the vessel walls. This is where accurate flow velocity information is particularly important because the wall shear stress is thought to be a major contributor to cardiovascular disease.

Selected references:

Inverse Problems 41 015008 (2025) doi:10.1088/1361-6420/ad9cb7

Journal of Cardiovascular Magnetic Resonance 25(1) 40 (2023) doi: 10.1186/s12968-023-00942-z 

Your research program

The objectives of the proposed study are to (i) extend adjoint-accelerated Bayesian inference of Flow-MRI data to 4D pulsatile flows within compliant boundaries; (ii) implement, test, and validate the results with compliant test objects in MRI machines; (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.

Expected Results: Implementation of compliant boundaries with an immersed boundary Flow-Structure Interaction framework and assimilation of 3D periodic velocity field data of flow through test objects with compliant boundaries of known mechanical properties. The image resolution and the quality of derived information such as pressure gradients and wall shear stress will be an order of magnitude greater than the existing state-of-the-art.

 Where you will work

For the main part of your work, you will be hosted in the Engineering Department at the University of Cambridge.

Integration within the FairCFD Network

Within the FairCFD network, you will contribute mainly to  WP3: Judicious combination of physics and data and/or. You will regularly exchange with other DCs of the network applying similar approaches to other problems, and/or applying different numerical methods to similar problems.
Two secondments (short research stays in other partners of the network) are planned during the PhD: with 1/ ONERA (2 months) to develop adjoint methods furthe; 2/ UPM (2 months) to compare with other data assimilation methods:

Interdisciplinary task: co-designing numerical frugality 

Beyond your individual research program described above, you will contribute along with all other FairCFD doctoral candidates to a network-wide multidisciplinary effort (WP5) addressing the environmental and societal dimensions of numerical simulation.
Each DC will participate in the definition of practical metrics for numerical frugality (computational cost, energy use, resource impact) and contribute data from their simulations to a collective meta-analysis. This initiative will be supported by interdisciplinary experts and accompanied by a dedicated DC in social sciences, who will lead a qualitative study on evolving practices in simulation across the network. Together, we aim to build concrete, informed recommendations for sustainable scientific computing.

Network Training Program — More Than Just a PhD

As a Doctoral Network funded by Marie Sklodowska-Curie Actions (MSCA-DN), FairCFD will offer to you a rich and engaging training experience, including

• Four one-week training events; (i) an induction week devoted to team-building, open-science practices and sustainability issues, (ii) an Essential Skills Accelerator event combining aiming to to equip DCs with essential technical and transferable skills, (iii) a Hackathon event where DCs will collaborate in teams to solve complex physics problem and compare various simulation strategies in terms of precision and sobriety, and (iv) a Career and Leadership Development Forum Aiming to equip DCs with transferable skills essential for their future careers.
• Five On-line courses combining technical training to state-of-the art simulation methods ranging from physics-based approaches to data-driven ones, exposition to industrial applications, along with Social, ethical and environmental aspects of decision-making in modelling practices.
• Involvement in the organisation of scientific events, including a mini-symposium as part of a large-audience scientific conference, a scientific symposium allowing to share the output in terms of new methods, innovation, and applications to industrial processes, and a Societal colloquium to deliver the outputs of the multidisciplinary tasks of the network.

This programme is designed to support your growth as a researcher, innovator, and engaged citizen, fully equipped to lead the next generation of responsible simulation science. See our website for more details (https://www.imft.fr/faircfd/project-presentation/)

Where to apply

Website: https://www.imft.fr/faircfd/project-presentation/

Cambridge website: https://mpj1001.user.srcf.net/MJ_jobs.html

Requirements

Research Field

Engineering » Mechanical engineering

 Education Level

Master Degree or equivalent

Skills/Qualifications

• Master’s degree (or equivalent) in fluid mechanics, applied mathematics, scientific computing, or related fields.
• Strong background in fluid mechanics, numerical methods, PDEs, and/or data-driven modeling.
• Interest in interdisciplinary research and open science.

Additional Information

Benefits

The successful candidates will receive an attractive salary in accordance with the MSCA regulations for Doctoral Researchers. The exact (net) salary will be confirmed upon appointment and is dependent on local tax regulations and on the country correction factor (to allow for the difference in cost of living in different EU Member States). The salary includes a living allowance, a mobility allowance, and a family allowance (if applicable). The guaranteed PhD funding is for 36 months (i.e., EC funding, additional funding is possible, depending on the local Supervisor, and in accordance with the regular PhD time in the country of origin).

Eligibility criteria

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. Apart from this rule, worldwide applications are expected and encouraged.

Selection process

The application process will be officially opened in February 2026. Meanwhile, additional information can be obtained by contacting the supervisors along with the DN coordinating team. For this, please contact us by e-mail using this contact link, mentioning “application to DC10” in the subject of the e-mail.

Additional comments

Start dates will be between April 2026 and September 2026 and will be negotiated with the successful candidate.