Job Description

We are seeking a Postdoctoral Research Fellow who will join our research team, actively engaged in pioneering research in statistical modelling, and their applications (in applied statistics). In this role, it is expected that you will contribute towards collaborative and independent research on an Australian Research Council (ARC) Discovery Project: “Advancing statistical models for clustering data with structured dependence” (DP250100860). This project aims to develop novel methods to identify important subgroups in data with various forms of dependence. It will propose new model-based clustering techniques that can capture complex relationships in data and enhance model validity. The primary intended outcome is to develop advanced methods and algorithms that can accurately identify clusters, patterns, outliers, and model evaluation. The potential benefits are to improve the validity of clustering models for analysing dependent data and direct effective crime prevention in Australia, with key application to Queensland Police Service (QPS) crime-incident data for improvement to the understanding of co-offending crimes, repeat victimisation, and crime hot spots.

Key responsibilities of this position include:

• Contribute with the Investigators to the development of statistical methodology and algorithms in the following research areas: GLMMs to effectively capture a variety of mixed intra- and between-subject correlations by random effects, mixed-effect mixture models for clustering recurrent event data and random graphs, robust estimation methods via expectation-maximisation (EM) based algorithms, predictive inference methods for assessing random effects in mixture models, and graphic visualisation methods for mixture models of random graphs.
• Undertake statistical analysis of sensitive QPS crime data at the secure research facilities in the Social Analytics Lab (SAL) of the Griffith Criminology Institute (GCI).
• Produce high-quality research outputs including publications in peer-reviewed journals.

The successful candidate will also contribute to the supervision of the PhD student funded by this ARC award as well as assist in statistical consulting activities in the Griffith Biostatistics Unit, Griffith Health, as appropriate. The candidate will have the opportunity to work with both national and international teams, and research partners outside of Griffith University. Thus, this position provides significant opportunities for growth and to pursue a career in academia or industry.

What we can offer
This is a fixed term (2.5 years, with the possibility of an extension subject to funding), full time position and will be primarily based at the Griffith University Brisbane South (Nathan) location.

As Griffith is a multi-campus University you may be required to work across other campus locations. Griffith University’s campuses are located on the lands of the Yugarabul, Yuggera, Jagera, Turrbal, Yugambeh and Kombumerri peoples.

Salary Range
The full time equivalent base salary for a Research Fellow (Grade 1) will range from $88,689 - $103,888 per annum + 17% superannuation. The total FTE package will range from $103,767 - $121,549 per annum.

The full time equivalent base salary for a Research Fellow (Grade 2) will range from $113,226 - $134,460 per annum + 17% superannuation. The total FTE package will range from $132,474 - $157,318 per annum.

Qualifications

The successful candidate will have: 

• A PhD in statistics (or closely related discipline), preferably in statistical modelling and mathematical statistics, with experience in cluster analysis, generalised and nonlinear regression, and mixture models. 
• Demonstrated track record to lead in the production of research outcomes such as publications in high impact peer-reviewed journals and conference proceedings (Grade 1) and track record to lead in awards/grants in related research areas (Grade 2).
• Demonstrated knowledge and experience in computational programming for statistical modelling (especially in R, Python and/or Julia).
• Excellent oral and written communication skills in English (international applicants whose first language is not English need to show how they meet English Language Proficiency requirements).
• Demonstrated good time management skills to ensure research activities are completed to the highest possible standards and within a set time frame.
• Demonstrated ability to work independently as well as within a team of researchers from different discipline areas and/or institutions.