Research Associate in Theoretical and Numerical Fluid Mechanics

We are seeking a committed and motivated Research Associate to join the School of Mechanical, Aerospace and Civil Engineering at the University of Sheffield, working on the EPSRC-funded project “Towards data-driven turbulence control: saving energy in pipelines by suppressing turbulence”. This exciting opportunity involves leading the development of advanced data-driven mathematical and computational models to suppress turbulence in pipe flows, contributing to pressing engineering efforts toward greener transportation and energy.

Building on recent advances, the successful candidate will use a powerful combination of dynamical systems theory, optimisation, DNS and machine learning to model and optimise forced relaminarisation in pipe flow, aiming to uncover the underlying physical mechanisms and design effective turbulence control strategies.

You will deliver this project in close collaboration with the Principal Investigator, Dr Elena Marensi, and her team, supported by synergies with experimentalists, interdisciplinary collaborations with machine-learning experts, and partnerships with industry stakeholders in the pipe manufacturing sector.

As part of the School's renowned Thermofluids Research Group (https://www.sheffield.ac.uk/mac/research/groups/thermofluids), you will work within one of the world’s leading hubs for fluid mechanics research. You will also benefit from interactions with the interdisciplinary Sheffield Fluid Mechanics Group (https://sites.google.com/sheffield.ac.uk/fluids). The role includes research visits to collaborators in Edinburgh and Vienna, engagement with industry partners, and several opportunities for personal and professional development, including tailored training, international conferences, student supervision, and support for career development.

Educated to PhD level (or nearing completion) in Fluid Mechanics, you will have a strong background in modelling and control of shear-flow turbulence, dynamical systems theory, and high-fidelity simulations/high-performance computing. Experience with machine learning and optimisation methods is desirable and would be an advantage in this role.

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