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Field
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energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA
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, current models only predict the potential for events rather than actual specific landslide occurrence. These models also struggle to directly quantify landslide hazards and to address key characteristics
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“Multiscale modelling of soil-structure interaction for renewable energy applications”. An improved understanding of soil-structure interface behaviour can lead to significant cost savings for offshore wind
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described by patients, there are few interventions that are currently in place to support recovery. To determine how to best intervene in a cost-effective way, we need up to date cost estimates and quality
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of the assembly of these complex microbial communities using ecological theory and mathematical models. The questions we address are: (1) how does the microbial community change during cultivation
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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Job Description Imagine a world where food production harmonises with natural processes, farmers nurture healthy soils, and biodiversity thrives. In contrast, current monoculture farming systems
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across
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the advancements brought by AI, there is currently no tool sufficiently intelligent to fully aggregate and utilize diverse data sources to create a comprehensive and adaptive dashboard for taking
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Duration: 4.5-year fixed-term appointment Employment type: Full-time Remuneration: You will receive a generous scholarship covering tuition fees and a tax-free stipend at current value of AU$37,000 per annum