Job Description
This is a research position available in the School of Mathematics and Statistics at UNSW. The successful candidate will undertake collaborative and self-directed research on an ARC-funded Discovery Project titled "High Dimensional Approximation, Learning, and Uncertainty". The aim of the project is to devise and apply innovative schemes for high-dimensional approximation.
The role reports to Professor Frances Kuo and Professor Ian Sloan and has no direct reports. Specific responsibilities include conducting research in the area of High Dimensional Approximation, Uncertainty Quantification, Deep Learning, and Quasi-Monte Carlo Methods independently and as part of a team.
Required Skills and Qualifications
* A PhD in Mathematics, preferably in computational mathematics or related area.
* Demonstrated ability to conduct independent research with limited supervision.
* Strong interpersonal skills with demonstrated ability to communicate and interact with a diverse range of stakeholders and students.
* Demonstrated knowledge in Quasi-Monte Carlo methods and/or finite element analysis and/or machine learning is highly desirable.
* Strong computer programming experience, preferably with Matlab, C++, or Python.
Benefits
* Level A - 113K - 121K plus 17% Superannuation and annual leave loading.
* Fixed Term – 2 years.
* Full-time (35 hours per week).
Others
The Research Associate will have the opportunity to work with leading researchers in the field and contribute to the preparation of research proposal submissions to funding bodies. The role also involves participating in and/or presenting at conferences and/or workshops relevant to the project.