The successful candidate will work on novel numerical approximations of PDEs under the guidance of Professor Santiago Badia. Current research lines include: neural network-based nonlinear discretisation of PDEs and their combination with other advanced (unfitted and hybridised) techniques; machine learning for PDE-constrained inverse problems, data assimilation, and nonlinear preconditioning. Current applications of this research include sea water intrusion, heart modelling and bushfire propagation. This research is supported by the Australian Research Council through two recent Discovery Projects and a Monash Data Futures Institute Project.
Applicants must have a PhD in Mathematics, Physics or Engineering, with a strong background in numerical methods for PDEs using finite element methods and/or related techniques. Background in the mathematical foundations of machine learning will be highly appreciated.
Depending on the experience level of the candidate, this role may be filled at Level A or Level B. Please refer to the position description attached below for different levels.
For more information and to apply, please follow this link.
Applications close: Thursday 28 April 2022
