Research Associate - Stochastic Partial Differential Equations
The University of Sydney
School of Mathematics and StatisticsClosing date: 8th April 2020
The University of Sydney is welcoming applications for a Research Associate in Stochastic Partial Differential Equations to join the School of Mathematics and Statistics. This position is funded by an Australian Research Council (ARC) Discovery Project Grant "Mathematics for breaking limits of speed and density in magnetic memories" held by Ben Goldys, Kim-Ngan Le and Thanh Tran. The technology of magnetic memories is grounded in the theory of strongly nonlinear systems of partial differential equation closely related to the nonlinear Schrodinger equation and to the so-called heat flow for sphere-valued harmonic maps. Solutions of such a system of equations develop singularities and vortex type solutions that are expected to lead to new types of magnetic memories operating in nano-scales. In such scales random fluctuations can modify behaviour of solutions hence the aforementioned equations must be modified to include random terms.
The aim of this project is to develop innovative mathematical theory and numerical algorithms for solutions of stochastic partial differential equations that describe properties of magnetization in ferromagnetic materials. The mathematical toolset will include analytical methods from stochastic analysis, partial differential equations, multiscale analysis and the theory of dynamical systems.
This is an opportunity to conduct research in a collaborative research team based at the Schools of Mathematics and Statistics at the University of Sydney, the University of New South Wales, and Monash University. Teaching is not a requirement, however opportunities to teach may be available if desired. You must have expertise either in stochastic analysis or partial differential equation, ideally in both. You will be familiar with one of these disciplines and be willing to learn the relevant parts of the other one.
For more information and to apply, click here.
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