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Part time (0.50 FTE) fixed term until 31 December 2026 Research fellow role supporting “Modelling for impact: A system dynamics tool to inform a fall prevention strategy for NSW” Base Salary
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
School and other relevant researchers at the Australian National University. In this role you will work within a team to help develop models of green iron and steel markets or other models, help develop
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at the Melbourne Centre for Nanofabrication (MCN). This role offers a unique opportunity to work on pioneering platforms that are transforming how we model human physiology and accelerate therapeutic discovery. In
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to perform in-depth and critical data analyses and, where relevant, data modelling. Ideal candidates will possess: Completion or near completion of a PhD in environmental chemistry, analytical chemistry
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the field. Ability to perform in-depth and critical data analyses and, where relevant, data modelling and visualisation through the use of statistical software relevant to the Position applied. Ability
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Completion or near completion of a PhD in the discipline area (molecular biology, immunology, cancer research, pharmaceuticals). Experience in animal models of disease. Evidence of publications in reputed
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systems (such as RedCAP), Endnote files, and databases Demonstrated experience with data analysis, visualization, and building machine learning models in programming language such as Python or/and R
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operational activities, ensuring your work delivers tangible environmental impact. Key responsibilities will include: Research: Lead and contribute to spatiotemporal modelling, mapping, and remote sensing
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applied research to support the sustainable management of freshwater ecosystems. We require a quantitative ecologist with skills/experience in statistical and quantitative modelling. The successful
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practice and policy · drive translational research activities such as evidence synthesis, economic evaluations, and implementation science approaches that support improved models of care for high‑risk