MSCA Fellowship - PhD Student

Organisation/Company Cambridge, University of Department Human Resources Division Research Field Engineering » Chemical engineering Technology » Biotechnology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Final date to receive applications 18 Apr 2026 - 23:59 (Europe/London) Country United Kingdom Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Is the Job related to staff position within a Research Infrastructure?

No

We are seeking to recruit an exceptional Doctoral (PhD) Candidate to join the new MSCA Doctoral Network FairCFD ("https://(Use the "Apply for this Job" box below)." ). The candidate will enrol for a PhD in Chemical Engineering at the University of Cambridge, under the supervision of Prof. Andy Sederman.

FairCFD offers a unique opportunity to engage in cutting-edge research in experimental advances linked to computational fluid dynamics (CFD) and more broadly engaging with and scientific computing. Within the FairCFD Doctoral Network, you will benefit from a unique breadth of experiences including:

• 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 cutting-edge 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.

Successful candidates will benefit from a diverse and interdisciplinary training program, including secondments in leading labs and collaborations with industry partners. Our network fosters a rich environment for innovation and skill development, equipping students to lead in both academic and industrial sectors.

Project scientific 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.

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://" ).

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: The implementation of new Flow-MRI acquisition and reconstruction strategies will enable measurement of flows at greater resolution and fidelity than has been possible previously. These new capabilities will be used to investigate applications that have not previously been possible including in the healthcare sector such as…