SUMMARY

*Opportunity to be part of a project involving spatial and temporal dynamics of Food Borne Illness associated with Fresh Produce
*Located at Australian Technology Park Campus, School of Life and Environmental Sciences
*12 Months Fixed Term, Full-Time, Academic Level A: Base Salary: $73,420 p.a. - $92,682 p.a. plus a generous employer’s contribution to superannuation.

ABOUT THE OPPORTUNITY

Applications are invited for a Postdoctoral Research Associate to the Australian Research Council (ARC) Industrial Transformation Training Centre (ITTC) for Food Safety in the Fresh Produce Industry.

The overarching purpose of the centre is to conduct industry-focused research to develop practical solutions to prevent or minimise microbial food safety risks in fresh produce across the value chain.

Understanding the epidemiology of food borne pathogens in a changing climate is an important step to reduce illness and mortality associated with consumption of contaminated fresh horticultural produce. The aim of this research project is to develop predictive and inferential models to help inform strategies to reduce the risk of food borne outbreaks modulated by climate, soil composition, hydrology, and other landscape features. The successful candidate will work closely with our Industry Partner, the NSW Food Authority.

ABOUT YOU

The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance in recruiting talent aligned to these values in the pursuit of research excellence and are seeking to recruit for a Post doctorate Postdoctoral Research Associate in Spatial and Temporal Dynamics role within the School of Life and Environmental Sciences, who has:

*BSc (Honours) or equivalent and PhD in Microbiology or closely-related discipline. (Microbial Risk Analysis)
*(Significant experience in statistical and mathematical modelling/ epidemiological and public health data
*Significant skills in data wrangling/facility with large data sets/data mining
*Demonstrated proficiency with the R platform and Python