Research Associate - Mathematics

Apply now Job no:523779
Work type:full time
Location:Sydney, NSW
Categories:Post Doctoral Research Associate

• One of Australia’s leading research & teaching universities
• Vibrant campus life with a strong sense of community & inclusion
• Enjoy a career that makes a difference by collaborating & learning from the best

At UNSW, we pride ourselves on being a workplace where the best people come to do their best work.

The School of Mathematics and Statistics currently has more than sixty continuing academic staff and more than thirty research staff as well as visiting academics. UNSW is the only university in Australia to be ranked in the top 100 in the world in Mathematics and Statistics by each of the four ranking bodies: CWTS Leiden, ARWU, USNews, QS. The School embodies a broad range of research interests in the areas of applied mathematics, pure mathematics, statistics, and data science. It has particular research strengths in Algebra, Analysis, Number Theory, Bayesian and Monte Carlo Methods, Biomathematics, Biostatistics and Ecology, Combinatorics, Computational Mathematics, Data Science, Dynamical Systems and Integrability, Finance and Risk Analysis, Fractional Calculus, Functional and Harmonic Analysis, Geometry and Mathematical Physics, Nonparametric Statistics, Ocean and Atmospheric Sciences, Optimisation, Stochastic Analysis, and Stochastic Modelling. The School's research groups are interconnected, with frequent interactions between groups and with other schools and faculties both within and outside UNSW.

The Research Associate 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. These schemes will be of proven reliability and accuracy, able to handle variables or uncertain parameters numbering in the hundreds or more, fast in execution, and tailored to specific applications.

The project will design novel schemes for forward simulation in the presence of multiple parameter choices, and surrogate methods for shortening computation times. The new surrogate methods will use recent developments in scientific machine learning, which blends data-driven learning and physics-based modelling. The whole project will make a significant contribution to uncertainty quantification, and should contribute ultimately to the rigorous development of efficient and mathematically sound digital twins. The primary technology of the project will be custom-designed Quasi Monte Carlo Methods.

About the role

• $106K - $113K plus 17% Superannuation and annual leave loading
• Fixed term – 2 years
• Full time (35 hours)

The role of the Research Associate reports to Professor Frances Kuo and Professor Ian Sloan and has no direct reports. 

Specific responsibilities for this role include:

• Conduct research in the area of High Dimensional Approximation, Uncertainty Quantification, Deep Learning, and Quasi-Monte Carlo Methods independently and as part of a team, including leading some areas of the project where the opportunity arises and where appropriate.
• Contribute to the writing of scientific papers and reports for international journals and progress reporting to other researchers and industry partners.
• Assist with the coordination of research activities and actively contribute to research outputs to meet project milestones.
• Contribute to the preparation of research proposal submissions to funding bodies and actively seek collaboration with industry partners as appropriate.
• Participate in and/or present at conferences and/or workshops relevant to the project as required.
• Joint supervision of honours and HDR students.
• Align with and actively demonstrate the UNSW Values in Action: Our Behaviours and the UNSW Code of Conduct.
• Cooperate with all health and safety policies and procedures of the university and take all reasonable care to ensure that your actions or omissions do not impact on the health and safety of yourself or others.

Selection Criteria

To be successful in this role you will have:

• 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.
• 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.
• An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines.
• Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training.

You should systematically address the selection criteria listed within the position description in your application.

For informal queries, please see the below contact details. 

Otherwise, please apply online - applications will not be accepted if sent directly to the contact listed .

Contact:
Frances Kuo
E: [email protected]

Applications close: April 30th , 2024

Find out more about working at UNSW at www.unsw.edu.au

UNSW aspires to be the exemplar Australian university and employer of choice for people from diverse backgrounds. UNSW aims to ensure equality in recruitment, development, retention and promotion of staff and that no-one is disadvantaged on the basis of their gender, cultural background, disability, sexual orientation or identity or Indigenous heritage. We encourage everyone who meets the selection criteria to apply.

UNSW partners with Australia’s leading diversity organisations, networks, and campaigns. Please refer to UNSW’s diversity offerings for further information on our flexible work and leave options, and support for carers (childcare, parent rooms, parental leave).

Advertised:25 Mar 2024 AUS Eastern Daylight Time
Applications close:30 Apr 2024 AUS Eastern Standard Time