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these data with multi-omics datasets to identify regulatory or metabolic bottlenecks. It involves statistical analysis and systems modelling, and close collaboration with strain engineering and bioprocess
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fundamental and demand-driven research on modelling and analysis of complex social networks. This line of research is broadly focused on modelling, analysis and estimation of complex network models in
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statistical models and analyse disparate datasets excellent communication skills with the ability to present findings clearly to diverse audiences strong collaborator experienced working with interdisciplinary
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factors or cognitive science/psychology, especially as applied to AI decision support, situation awareness, information seeking, etc. Demonstrated expertise in applying models of higher-order cognitive
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and trait association (including epiGWAS and predictive modelling), generating world-first insights that could transform how breeders enhance yield and resilience in sorghum, barley and wheat. Supported
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in crop agronomy and farming systems, with emphasis on Queensland’s key cereal and pulse crops – applying advanced methodologies, ag-tech, and crop models to enhance productivity, sustainability, and
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level B and C minimum 3 years post-PhD experience for Level C significant experience conducting original research and engaging in scholarly activity strong analytical skills in modelling time trends and
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interaction energy models, Kinetic Monte Carlo simulations for crystal growth, and advanced methods for calculating elastic and thermal properties of molecular crystals. The work will involve integrating and
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Academic Level B: Completion of a PhD in the field of Computer Science/Artificial Intelligence. Software engineering expertise, including design and implementation of AI-based models (machine learning, deep
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lateral sclerosis) and dementias using cell models. The successful candidate will work closely across the NeuroProteomics and Molecular Therapeutics groups. You will contribute to the development of a