Models are becoming an increasingly important tool in many branches of modern society due to advances in science and technology. As our understanding of these models improves, the complexity of the types of questions being asked increases. The objective of this major is to train students in techniques of model development, use and assessment.

A key requirement for future scientists, industry leaders, resource managers, and policy makers is an ability to build and evaluate models and/or interpret model outputs. Career opportunities for graduates extend into every part of society, including: research (e.g. CSIRO, Universities); public sector (e.g. Bureau of Meteorology, Murray Darling Basin Authority, state government agencies); and private sector (e.g. engineering, finance).

Students may choose to complement this major with a quantitative applications major or minor consisting of courses from areas such as: physics; earth and environmental science; global change science; climate science and policy; environmental geoscience; geophysics; quantitative finance; or mathematical finance.

Coupled with a detailed disciplinary base, this major will provide students with the necessary skills to tackle the problems facing tomorrow's society.

Graduates from ANU have been rated as Australia's most employable graduates and among the most sought after by employers worldwide.

The latest Global Employability University Ranking, published by the Times Higher Education, rated ANU as Australia's top university for getting a job for the fourth year in a row

Learning Outcomes

1. Apply mathematical concepts, including Calculus, Linear Algebra and Differential Equations to analyse specific problems and identify the appropriate mathematics to realise a solution.
2. Use computer programming and statistical analysis skills to efficiently model systems.
3. Recognise the connections between mathematics and other disciplines, and how mathematical ideas are embedded in other contexts.
4. Represent real-world systems from science and technology in a mathematical framework.
5. Employ appropriate methods to analyse, solve and evaluate the performance of mathematical models.
6. Identify relevant disciplinary material and sources to pursue independent mathematical learning and deepen  understanding of the behaviour of a system reasoning.
7. Relate the behaviour of the output of the mathematical model to the underlying physical or conceptual model of interest.
8. Extend their experiences of working both independently and collaboratively within the discipline to other contexts.
9. Relate professional and disciplinary information and ideas to varied audiences in effective and appropriate ways.
10. Reflect the professional standards of the discipline and of science in their own work and practice.