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Field
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Australian knowledge, but it also speaks to the mission of universities – to develop and spread wisdom to make the world a better place. Harnessing technology, we thrive as a distributed yet connected
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develop algorithms to identify and predict SRL subprocesses from multimodal learning data (e.g., EEG/fNIRS, eye-tracking, and think-aloud protocols); • Analyze large-scale learning analytics data
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welcome applications from passionate, skilled, and committed individuals. About the Role The spatial distribution of schistosomiasis coincides with development of certain water management infrastructure
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affordability, welfare, and income distribution. Contribute to the preparation of policy briefings, academic publications, and public-facing reports. Present findings in academic and policy settings, including
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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Experience with machine learning algorithms and ideally experience developing novel methods Understanding of basic biological principles and experience interpreting ‘omics data Ability to analyse information
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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distributed programs, which include four university academic campuses, are located on traditional, ancestral and unceded territories of First Nations Peoples and communities around the province. Our Vision
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proposals for competitive funding agencies or organizations Works in the field gathering data -e.g., distributional data, measurements, species samples. Performs other job-related duties as assigned