Postdoctoral Research Fellow in Data Science for Smart Horticulture (ARC TC-SaSH)
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About the role
You will conduct data science and modelling research within the Industrial Training Centre for Smart and Sustainable Horticulture (SaSH), focused on improving productivity and profitability of horticultural enterprises in protected settings. You will integrate across SaSH research and training programs to support crop performance in high- and medium-tech growth facilities, with an emphasis on tomato and snacking capsicums. You will design experiments and data acquisition approaches that maximise statistical power, interpretability and modelling value. Working with researchers across disciplines, you will align sampling protocols, metadata standards and measurement schedules, and build scalable workflows to integrate heterogeneous datasets such as imaging, sensors, environmental variables and operational data where relevant. You will develop and validate advanced modelling approaches, including statistical learning, mechanistic modelling, hybrid physics-informed machine learning, and stochastic modelling to represent biological variability and uncertainty. You will translate research outcomes into reusable tools such as reproducible pipelines, code repositories, dashboards and model interfaces, and support best-practice adoption with industry partners. You will contribute to manuscripts and reports, present findings to scientific and industry audiences, and mentor students and junior researchers in data science methods and reproducible research workflows. What Success Looks Like: Deliver validated predictive and explanatory models for crop growth, stress, yield and quality. Produce scalable data integration workflows that enable cross-program inference and discovery. Deliver publications, reports and presentations aligned to project milestones and stakeholder needs. Provide reusable, reproducible tools that support adoption by research and industry partners. Maintain high standards in research data management, documentation and secure handling of datasets. Please refer to the Position Description for full details. About You You hold a PhD in a relevant quantitative discipline, or bring equivalent qualifications or research experience. You have demonstrated experience in advanced data science and statistical modelling of complex datasets. You bring expertise in dynamic systems modelling, including mechanistic and/or stochastic approaches. You have strong programming skills in Python and experience working with scientific computing and reproducible workflows. You're a clear communicator with strong organisational skills, and you're comfortable working across disciplines and managing competing deadlines.
Benefits
Additional Information
Full-time, fixed-term position for 3 years. Based at Hawkesbury Campus with flexible working arrangements. Salary: Academic Level A $97,153 - $117,376 per annum, plus 17% Super and Leave Loading. Create reproducible data pipelines and models that improve forecasting in controlled environments.
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Company Intel
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