Postdoctoral Research Fellow in Data Science for Smart Horticulture at Western Sydney University Western Sydney University in Australia

Western Sydney University is advertising a Postdoctoral Research Fellow in Data Science for Smart Horticulture within the ARC Training Centre for Smart Horticulture (SaSH) in Richmond/Hawkesbury, New South Wales, Australia.

The role sits at the intersection of computer science, statistics, agriculture, and environmental science, with a strong emphasis on artificial intelligence, machine learning, data science, precision agriculture, digital agriculture, and sensor analytics. The successful candidate will work on reproducible data pipelines, predictive and explanatory models, and modelling approaches for crop growth, stress, yield, and quality in protected horticultural systems.

Research activities include integrating heterogeneous datasets such as imaging, sensors, environmental variables, and operational data; designing experiments and data acquisition strategies; developing statistical learning, mechanistic modelling, hybrid physics-informed machine learning, and stochastic modelling methods; and translating outputs into dashboards, code repositories, and reusable tools for research and industry partners.

Eligibility highlights: a PhD in Data Science, AI, Computer Science, Statistics, Agricultural Engineering, Plant Science, or a related quantitative field; experience with machine learning and predictive analytics; strong Python programming skills (R also mentioned in the social post); and excellent communication and teamwork skills. The full job description also values dynamic systems modelling and reproducible research workflows.

Funding and conditions: this is a paid 3-year fixed-term academic appointment, with salary of AUD $97,153–$117,376 per annum plus 17% superannuation and leave loading. The university states that employer visa sponsorship will be provided if required.

Application window: closes 19 July 2026 at 11:59 pm. Applicants must submit through Western Sydney University’s online recruitment system and include reference code REF1452K.

Contact: Professor Oula Ghannoum ([email protected]).