Location: Clayton campus
Employment Type: Full-time
Duration: 2-year fixed-term appointment
Remuneration: $99,147 - $117,738 pa Level B (plus 17% employer superannuation)

• Be inspired, every day
• Drive your own learning at one of the world’s top 80 universities
• Take your career in exciting, rewarding directions

Everyone needs a platform to launch a satisfying career. At Monash, we give you the space and support to take your career in all kinds of exciting new directions. You’ll have access to quality research, infrastructure and learning facilities, opportunities to collaborate internationally, as well as the grants you’ll need to publish your work. We’re a university full of energetic and enthusiastic minds, driven to challenge what’s expected, expand what we know, and learn from other inspiring, empowering thinkers.

The Opportunity

The Department of Econometrics and Business Statistics, one of seven academic departments in the Monash Business School, comprises approximately 50 academics with particular strengths in econometric theory and methods, Bayesian econometrics, applied econometrics, time series analysis, forecasting, statistics, actuarial science, data visualisation and analytics.

As a testament to the quality of the Department's research output, Monash was given the highest possible rating (5) in Econometrics in the 2012, 2015 and 2018 Excellence in Research for Australia assessments conducted by the Australian Research Council (ARC). The Department is also ranked in the top 10 institutions in the fields of Econometrics, Time Series and Forecasting by IDEAS (a Research Papers in Economics service maintained by the Federal Reserve Bank of St. Louis, USA).

The Research Fellow will conduct research associated with ARC Discovery Grant DP200101414: “Loss-Based Bayesian Prediction”. This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method is an expected outcome, as is extensive application of the method to diverse empirical problems, including those based on high-dimensional and hierarchical data sets. The project will exploit recent advances in Bayesian computation, including approximate Bayesian computation and variational inference, to produce predictive distributions that are expressly designed to yield accurate predictions in a given loss measure. The Research Fellow would be expected to engage in all aspects of the research and would therefore build expertise in the methodological, theoretical and empirical aspects of this new predictive approach.

The appointee will have a doctoral qualification in econometrics or statistics, with specific expertise in one or more of the following areas:  Bayesian statistical methods, including modern computational techniques; forecast methodology and/or theory; high-dimensional statistical analysis; statistical theory.

If you are enthusiastic at the prospect of embarking on a ground-breaking challenge, we strongly encourage you to apply! 

This role is a full-time position; however, flexible working arrangements may be negotiated.

At Monash University, we are committed to being a Child Safe organisation. Some positions at the University will require the incumbent to hold a valid Working with Children Check.

For instructions on how to apply, please refer to “How to apply for Monash Jobs”.

Enquiries

Professor Gael Martin, Chief Investigator, +61 3 9905 1189

Position Description

 Research Fellow

Closing Date

Sunday 31 May 2020, 11:55 pm AEDT