Postdoctoral Research Fellow in Data Science for Smart Horticulture (ARC TC-SaSH)

Create reproducible data pipelines and models that improve forecasting in controlled environments. Design experiments and data acquisition approaches to maximize statistical power, then develop and validate advanced modelling approaches including statistical learning, mechanistic modelling, and hybrid physics-informed machine learning. Build scalable workflows to integrate heterogeneous datasets such as imaging, sensors, and operational data, translating research outcomes into reusable tools like dashboards and model interfaces. Contribute to manuscripts, present findings to scientific and industry audiences, and mentor students and junior researchers in data science methods. Deliver validated predictive and explanatory models for crop growth, stress, yield, and quality while maintaining high standards in research data management.