Research Fellow in Econometrics and Business Statistics

Monash University Department of Econometrics and Business Statistics


Research Fellow

Job No.: 611861

Location: Clayton campus

Employment Type: Full-time

Duration: 12-month fixed-term appointment

Remuneration: $99,147 - $117,738 pa Level B (plus 9.5% 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 undertake and contribute to research under the auspices of ARC Discovery Project DP170100729: “The Validation of Approximate Bayesian Computation: Theory and Practice”. This project seeks to explore and extend the use of non-summary-based distance metrics within approximate Bayesian methods. A crucial aspect of the project will be the evaluation of these approaches for model choice, and the development of model choice algorithms for non-summary-based approximate Bayesian computation methods. The project will exploit recent advances in approximate Bayesian computation methodology, including expectation propagation, and Gibbs-like approaches. The Research Fellow is expected to engage in all aspects of the research and will therefore acquire expertise in the methodological, theoretical and empirical aspects of the project.

The appointee will have a doctoral qualification in econometrics or statistics, with specific expertise in the following areas: Bayesian computational statistical methods, including approximate computational techniques; 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”.


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

Position Description

Download File Research Fellow

Closing Date

Sunday 17 January 2021, 11:55pm AEDT

Please note: Monash University will be closed from 23 December 2020 until 3 January 2021 inclusive. 

In your application, please refer to


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