Postdoctoral Research Fellow in Bayesian Statistics, QUT, Brisbane

Postdoctoral Research Fellow in Bayesian Statistics, QUT, Brisbane

We seek a motivated person to advance the development of novel Bayesian methodology including statistical experimental design with excellent theoretical and computational skills. Ideally, applicants will have recently been awarded a PhD, or are in the final submission stage, and can provide evidence of research expertise and the ability to interact effectively with research collaborators. Women, Indigenous Australians and Torres Strait Islander people are strongly encouraged to apply.

Position Title: Postdoctoral Research Fellow in Statistics
Reference: 12086
Closes: 22 February 2013
Organisational Area: Statistical Science Discipline, Mathematical Sciences School, Science and Engineering Faculty
Salary Range/Classification: $78 542 to $93 277 pa (LEVB) Plus Superannuation: employer contribution
Status: Fixed-term for 2 years
Contact: Tony Pettitt, Professor, +61 7 3138 2309, Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo. HR Contact: Belinda Smith, HR Advisor, +61 7 3138 4151 Open to: Australian and International applicants

When applying for this position we encourage you to upload your response to the selection criteria.

This position involves research into applied Bayesian statistics including Bayesian design of experiments. The position would suit a candidate holding a PhD in statistics, or in the final stages of submission, in areas related to Bayesian statistics, experimental design, Bayesian design or who has only a few years of experience since obtaining their PhD. They should have strength in analytic and computational methods. Other areas of statistics or related disciplines will be considered.

The Postdoctoral Research Fellow will be part of a team involved in research associated with Australian Research Council Discovery Projects investigating novel approaches to applied Bayesian statistics and Bayesian experimental design which involves collaboration inside and outside the University, both nationally and internationally.

The successful appointee will advance applied Bayesian statistics including experimental design through the development of novel Bayesian methodology with a grounding in high impact applications. They will collaborate with experts in Bayesian statistics and will be a part of the growing Statistical Science Discipline. They will have an opportunity to be involved in various aspects of the School's diverse research and academic activities.

The appointee will conduct research on behalf of, and in conjunction with, the project team and report to the Project Leaders, Professor Tony Pettitt (Professor, Mathematical Sciences) and Dr James McGree (Senior Lecturer, Mathematical Sciences). The position will liaise with stakeholders in the research project which involves statisticians who are collaborators in Lancaster, Southampton UK and Dublin, Ireland and with statisticians and other disciplines in Brisbane.

In the recent 2012 Excellence in Research for Australia (ERA) exercise undertaken by the Commonwealth Government, the quality of our research capability was assessed at '4 out of 5' or 'above world standard' for Statistical Science as well as in the broader Mathematical Sciences.

Mathematical Sciences is located on the Gardens Point campus of QUT, which borders the City Botanical Gardens and the Brisbane River in the city centre of Brisbane and the Statistical Science Discipline is situated in the new state-of-the-art $230 million Science and Engineering Centre in the heart of our Gardens Point campus. For further information about Brisbane, please visit

The Statistics Discipline within the School has an international reputation for Bayesian statistics and machine learning through the work of Australian Research Council (ARC) Laureate Fellow, Professor Peter Bartlett, Professor Kerrie Mengersen and ARC Professorial Fellow, Professor Tony Pettitt. The Statistics Discipline carries out research into theoretical and computational Bayesian statistics, machine learning, applied statistics and design of experiments. The Statistics Discipline is a highly collaborative group with a focus on cross-disciplinary research of high quality and impact to solve real world problems of global significance.

Further information on the school is available at: