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This project intends to address the under-representation of culturally and linguistically diverse (CALD) patients in the uptake of Australia’s first Virtual Emergency Department platform hosted
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representation learning paradigm. This approach allows each user to disclose an incomplete yet distinct set of personal information while still learning expressive user representations from that data
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frameworks that model the users who are going to interact with them. These models are typically a simplified representation of users (e.g. using the relevance of items delivered to the user as a surrogate for
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to the designated supervisor for consideration. The awarding of the PhD scholarship is contingent upon successfully passing the interview process. Basic knowledge in cell biology is highly desired. Basic knowledge in
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quantitative methods. Expected outcomes include new knowledge on designing for technology-supported wellbeing, particularly when using human-centred AI. The project will involve critical analysis of existing
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. Degree and/or knowledge of core chemical engineering disciplines such as reaction engineering, kinetic and process simulation. Excellent written and verbal communication skills. Problem-solving experience
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) economy Desirable • Knowledge of carbon accounting, GHG methodologies, sustainability reporting frameworks (SASB/ISSB), circular economy and textile sustainability. Well-developed verbal and written
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skills with R, C, C++ or Python. Desired: Knowledge or experience with spatial statistics, spatial modelling or GIS; Some knowledge with ecology; Excellent writing and communication skills; Experience with
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of fashion and textiles, and knowledge/understanding of the Australian fashion industry, Life Cycle Analysis, environmental protection, product stewardship and circular economy Collaborative, confident
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will generate new knowledge in the area of IoT Trust by developing novel techniques to establish trust in highly dynamic crowdsourcing IoT environments. The project's main outcomes include