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Intelligence at The University of Queensland, this newly established lab is on a mission to empower expert decision-makers by harnessing the power of AI to tackle complex challenges. With a strong emphasis on
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. The project, "Building the bridge from GWAS to Breeder through multi-omic network data fusion", focuses on transforming GWAS discoveries into actionable breeding insights. Using sorghum as a model, the work
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closely with an industry partner, you will generate cutting-edge insights into protein structures and protein-ligand interactions using in silico approaches such as bioinformatics, modelling and docking
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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Medical School Seeking a highly skilled project manager with experience in complex, multi-stakeholder research initiatives. Part-time (0.5 FTE) appointment on a fixed term basis for 12 months. Base
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essential, mathematical theory frequently suggests its value is low. This misalignment creates a gap between modelling and practice. The research will focus on developing new theory to explain why
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projects involving multi-electrode array recordings in behaving animals, using techniques including electrophysiology, psychophysics, computational modelling and advanced quantitative analysis. This position
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modelling at laboratory and/or field scale, with the ultimate aim of producing a design methodology document that synthesises the research outcomes from the project, which can be used both by the partner
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experience 17% Superannuation and Flexible Working Arrangements Based at the RMIT Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT's Materials Modelling and
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 hours ago
: Bayesian Machine Learning – Led by Dr Thang Bui, this project focuses on sequential decision-making and bridging deep learning theory and practice. Applicants with expertise in probabilistic modelling