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candidate with a background in social anthropology, design anthropology and/ or visual anthropology, and an interest in developing innovative new research in futures anthropology to win a scholarship within a
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mathematical integration, with the desired outcome being to increase the water and/or nitrogen use efficiency of Australian cropping systems. We propose to use a range of emerging data science approaches within
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, inclusive and effective understandings and impact in response to the urgent need to better plan for futures with and for people and other species, emerging technologies and climate. The Lab’s internationally
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of learnt features in deep neural networks. Specifically, we aim to: • Develop methodologies to interpret and visualize the features learnt by neural networks in a human-understandable manner. • Investigate
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drones can capture visual and thermal imaging at a very high resolution, suitable for detecting photovoltaic modules and the cleanliness of solar panels. These images and other data can be processed by
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effective digital platform for analyzing, visualising and interpreting the condition of civil engineering structures. The digital twin models are created to represent the state and behaviour of structures in
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to individualise tumour type, associated biomarkers and genomic characteristics to high precision. The resulting multipurpose machine learning workflows will lead to rapid precision diagnoses and effective
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machine learning frameworks like PyTorch. The candidate should demonstrate strong analytical abilities, an eagerness to learn and solve complex problems, and possess effective collaboration skills
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, current DES solvents show excellent performance only in selected batteries, and there are limited studies on their performance with mixed battery chemistries. This project aims to develop effective
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This project aims to develop outcome measures for early detection of neurodegenerative disorders using artificial intelligence (AI) and various sensing modalities, offering personalised support to