- 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 with & learning from the best
At UNSW, we pride ourselves on being a workplace where the best people come to do their best work.
THIS ROLE IS LOCATED IN SYDNEY, AUSTRALIA.
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 and pure mathematics and statistics.
The role of Research Associate sits in the UNSW Data Science Hub located within the School of Mathematics & Statistics. The UNSW Data Science Hub is a major strategic initiative of UNSW Science. It aims to cultivate and promote foundational and applied research in Data Science with a focus on environmental, health, and business data challenges, and a Data for Good program. The Hub provides a world-class environment, with access to state-of-the-art data visualisation and computing facilities. These facilities enable the creation, development and deployment of transformative data-driven decision-making tools that help us to address future societal challenges.
The role works in close collaboration with leading scientists and engineers at Data61 and other CSIRO Business units as part of the Decision activity area of the Machine Learning and Artificial Intelligence Future Science Platform. The role is based at the UNSW Data Science Hub, UNSW Sydney Kensington Campus but will be required to perform some work at Data61, Eveleigh, NSW.
About the role
- $98K - $107K plus 17% Superannuation and annual leave loading
- Fixed Term – 22 months
- Full-time (35 hours per week)
The role reports to the Director of the UNSW Data Science Hub and works closely with Professor Scott Sisson (UNSW) and Terry O’Kane (CSIRO). The role has no direct reports.
Specific responsibilities for this role include:
- Conduct research in the theory, methods and application of complex statistical and machine learning techniques to geophysical climate data.
- Investigate computational methodologies for dimension reduction, inferring latent patterns from time series data, employing latent entropy and dimension measures and identifying (conditionally) causal relationships between them.
- Act as a conduit to efficiently draw together the expertise of researchers from both UNSW Sydney and Data61/CSIRO to best tackle the research challenges.
- Perform research collaboratively and individually, which will lead to high quality research articles for publication in internationally refereed mathematics journals.
- Initiate, implement and complete new research projects.
- Present results of research at conferences and other venues.
- Coordinate research activities and participate in the setting of future research directions of the project.
- Satisfactory conduct of administrative and other duties allocated by the supervisor.
- Participate in supervision of research postgraduate students.
- Effectively engage with the broader scholarly community.
- 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.
About the successful applicant
- PhD or equivalent qualification in mathematics, statistics, physics or an alternative field with a strong mathematical and statistical background.
- Strong knowledge and demonstrated ability in at least one of Bayesian statistics, computational statistics and machine learning methodology, including variational inference, Markov chain Monte Carlo methods, Gaussian processes, mixture modelling.
- Familiarity with deep learning and different neural network architectures.
- Superior ability to program in a high level language, such as Python, R, Java, Matlab or C++, with application of version control.
- A strong research track record (relative to opportunity) as evidenced by publications in leading journals or conferences.
- Capacity to initiate and conduct independent research.
- A capacity to attract and effectively supervise research students.
- Demonstrated capacity to interact successfully with the broader scholarly community and with both internal and external stakeholders.
- Ability to work cooperatively in a team and contribute positively to research culture.
- Proven ability to communicate effectively both verbally and in writing.
- 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.
Desirable but not compulsory:
- Strong knowledge of manifold learning or topological data analysis.
- Familiarity with data engineering and its interface with machine learning.
- Experience with analysis of high dimensional multivariate multiscale data sets.
- Familiarity with vector autoregressive processes with external factors.
- Knowledge of (non-linear) dimension reduction methods.
You should systematically address the selection criteria listed above in your application.
Please apply online - applications will not be accepted if sent to the contact listed.
Applications close: September 1st, 2022
Find out more about working at UNSW at www.unsw.edu.au
UNSW is committed to equity diversity and inclusion. Applications from women, people of culturally and linguistically diverse backgrounds, those living with disabilities, members of the LGBTIQ+ community; and people of Aboriginal and Torres Strait Islander descent, are encouraged. UNSW provides workplace adjustments for people with disability, and access to flexible work options for eligible staff. The University reserves the right not to proceed with any appointment.