FairCFD

PhD Position (MSCA-DN FairCFD, DC11)

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 health/food industries by developing advanced and efficient data measurement 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 program

Background

Flow‑MRI (magnetic resonance imaging of flowing fluids) is a non‑invasive imaging technique that measures 3D and time‑resolved velocity fields in opaque fluids, making it uniquely valuable for studying flows under realistic operating conditions in a range of engineering and medical applications. There are many challenges in optimising the quantitative applicability of Flow-MRI including signal‑to‑noise ratio, spatial resolution and temporal resolution where noise and partial‑volume effects can strongly affect derived quantities such as shear rate and wall stresses.

Recent advances in acquisition strategies and model‑based data assimilation improve the ability of Flow‑MRI to be used not just to visualise flow but to infer rheological behaviour directly from experimental data. This project will develop these capabilities by combining high‑information‑content Flow‑MRI datasets with physics‑based modelling and Bayesian inference to determine constitutive models for non‑Newtonian and other complex fluids in situ.

Selected references:

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

Journal of Magnetic Resonance, 274, (2017),  103-114, doi.org/10.1016/j.jmr.2016.11.003

Your research program

The objectives of the proposed study are to (i) design and run Flow‑MRI experiments on a range of non‑Newtonian fluids in steady and periodic flow (ii) develop and optimise MRI acquisition strategies to improve the efficiency of data collection and enhance spatial and temporal resolution (iii) increase the quantitative accuracy of Flow‑MRI data through improved reconstruction,  and uncertainty estimation and (iv) assess the ability to accurately model these complex fluids by using adjoint‑accelerated Bayesian inference with the experimental Flow‑MRI data.

Expected Results: Implementation of Flow-MRI basic acquisition strategies to generate steady Flow‑MRI datasets of flow of non-Newtonian fluids. Create benchmark datasets for the participants and wider research community. New acquisition methodologies will be developed after critical flow characteristics have been identified. These methods will be used to acquire steady-state and periodic Flow‑MRI data for complex fluids (shear‑thickening, viscoplastic, viscoelastic) and to assess rheology models. Production of a method to determine the most accurate rheometry model directly from Flow‑MRI rather than separate rheometry tests. Learn new constitutive laws from high‑information‑content Flow‑MRI data.

Where you will work

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

Network activities

Integration within the FairCFD Network

Within the FairCFD network, you will contribute mainly to  WP3: Judicious combination of physics and data and WP2: Efficient data-based approaches. 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/ KTH (2 months) to compare rheological measurements; 2/ UNISA (2 months) to develop sensitivity 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/)

Skills/Qualifications

• Master’s degree (or equivalent) in a related area including chemical engineering, engineering, chemistry, physics, applied mathematics or related fields.
• Strong background in quantitative data analysis, experimental design, fluid mechanics, and/or data-driven modeling.
• Interest in interdisciplinary research and open science.

 Practical Informations

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.