Overview
We are recruiting for an exceptional Doctoral (PhD) Candidate to join the new MSCA Doctoral Network FairCFD (https://www.imft.fr/faircfd/project-presentation/). The candidate will enrol for a PhD in Chemical Engineering at the University of Cambridge, under the supervision of Prof. Andy Sederman.
Opportunity
FairCFD offers a unique opportunity to engage in experimental research linked to Computational Fluid Dynamics (CFD) and broadly scientific computing. The postholder will benefit from a breadth of experiences:
* Contribute to technological innovation in health/food industries by developing advanced and efficient data measurement strategies.
* Join a vibrant network of 15 doctoral candidates across 9 European countries, with access to network events, high-level training to technical and transverse skills, and secondments in both academic and industrial environments.
* Take part in a network-wide interdisciplinary effort to define and promote numerical sustainability in scientific research.
Project Description
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.
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. The project will require development of advanced experimental and data analysis skills and will work closely with the doctoral candidate working in Cambridge on data assimilation (https://www.imft.fr/dc10-arterial-circulation/).
Objectives
* Design and run Flow MRI experiments on a range of non‑Newtonian fluids in steady and periodic flow.
* Develop and optimise MRI acquisition strategies to improve the efficiency of data collection and enhance spatial and temporal resolution.
* Increase the quantitative accuracy of Flow MRI data through improved reconstruction and uncertainty estimation.
* Assess the ability to accurately model these complex fluids by using adjoint‑accelerated Bayesian inference with the experimental Flow MRI data.
Secondments
The project involves secondments at the KTH (Stockholm, Sweden) and the University of Salerno (Salerno, Italy).
Eligibility
Applicants for the studentships must have a First Class (or a high 2:1) or equivalent degree in a relevant discipline such as chemical engineering, engineering or physics and must not have spent more than 12 months in the UK for the past 3 years.
Application Details
Fixed‑term: The funds for this post are available for 3 years in the first instance.
Applications closing date: 26th of April 2026. Please quote reference NQ49025 on your application and in any correspondence about this vacancy.
Successful candidates will be employed as a Research Assistant and must apply for admission as a PhD student with the University and meet our admission criteria.
Contact
For further details on the role please contact Prof. Andy Sederman at ajs40@cam.ac.uk. If you have questions on the application process, please email Mr Vito Candela, HR Administrator, at hr@ceb.cam.ac.uk.
EEO Statement
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
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