Join an ARC-funded research project developing numerical methods to simulate the flow of dense viscous liquids through a deformable granular mush. The project aims to improve fluid-dynamical understanding of how magmatic ore deposits form. Such deposits contain critical minerals including nickel, copper, cobalt and platinum-group elements that underpin the global transition to Net Zero.

You will be responsible for developing novel numerical methods to model continuum problems in a two-liquid/granular medium and for interpreting the results physically. These models will capture how a dense fluid migrates, spreads and becomes trapped within a deformable, yield-stress material. Working alongside a co-supervised PhD student and the wider investigator team, you will build and extend simulation tools to explore droplet migration, instability and gravity-driven flows within this setting. There is scope to bring your own perspective to the numerical approach, and your work will feed directly into peer-reviewed publications in leading fluid mechanics journals and presentations at both specialist and general conferences.

The position is part of an interdisciplinary team spanning applied mathematics and economic geology and is based in the School of Mathematics at Monash University. You will work closely with A/Prof Anja Slim at Monash, Dr Edward Hinton at the University of Melbourne and A/Prof Duncan Hewitt at the University of Cambridge. The position includes extended visits to Cambridge's Department of Applied Mathematics and Theoretical Physics. You will also have regular opportunities to work with the project's economic geology team, testing your models against real field and experimental data and seeing your results feed directly into questions about how magmatic ore deposits form and where they might be found.

This is a chance to help develop some of the first physical models of how magmatic ore deposits form. Despite their economic importance, these systems remain largely unexplored from a fluid-dynamical perspective, offering substantial scope for fundamental new insights.