19 modelling-complexity-geocomputation Fellowship positions at The University of Queensland
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modeller to help lead a collaborative research programme between the University of Queensland and King Abdulla University of Science and Technology (KAUST) with Prof David Suggett. Our shared goal is to
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of Mechanical and Mining Engineering, where innovation meets impact. As a key member of this dynamic team, you will develop cutting-edge computational models of two-phase flow in fractured media — ranging from
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complex multiphysics problems, and want to drive meaningful change through research - this might be the role for you! This is your opportunity to work on high-impact projects—from modelling fluid flow in
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experience with coding in Matlab, Python, or R. Strong track record in computational science, image analysis, or mathematical modelling of complex systems. Evidence of publications in reputed refereed journals
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ecologists and modellers. Marine Spatial Ecology Lab (MSEL) is a dynamic group conducting world-leading coral reef research. MSEL’s portfolio spans reef ecology, ecosystem resilience, fisheries rebuilding
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(CRISPR screens, Chromatin IP, ATAC-seq) and cellular biology (cell death mechanisms, immunology), and experience with animal models and organoid cultures. Position 3 will focus on Bioinformatics
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) Based at our St Lucia Campus About This Opportunity We are seeking a Postdoctoral Research Fellow to join an ARC Linkage Project focused on advancing geomechanical modelling for underground CO₂ and H
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independently and design/interpret experiments using in vivo animal models and ex vivo methods, adhering to PPE requirements (including PAPR use with restricted carcinogens). Demonstrated critical thinking
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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