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to the advancement of healthcare technologies, systems, and services through applied design practice. With a portfolio spanning mobile imaging, wearable technologies, and distributed models of care, DHC leads
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student
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@rmit.edu.au Please send your CV to akram.hourani@rmit.edu.au Required Skills: Programming and simulation: strong experience in Python or MATLAB. Mathematical modelling: probability, optimization, or multi-agent
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of potential options for implementation. This will include prototyping and testing of various implementation options, analysis and documentation of results. Project 2 - Future Power System Modelling: As part of
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models like SWMM are computationally slow and lack scalability, while opaque AI methods risk biased outcomes. This project addresses these gaps by developing a responsible machine-learning framework
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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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Institute for Biomedicine and Glycomics / Institute for Drug Discovery | Australia | about 2 months ago
sequencing approaches to i) establish innovative zebrafish models of human diseases, ii) investigate the underlying pathogenic mechanisms and iii) run drug discovery programs (https://giacomottolab.com/). We
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care. There is evidence that clinical debriefing models can mitigate the psychological effects of these stressful events and improve the psychological safety of their working environment to improve
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palliative care models. It comprises a number of sub-projects including a systematic literature review, a synthesis of existing qualitative data, and the design and analysis of a quantitative preference survey
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machine learning approaches show issues in model performance and efficiency and vulnerability towards the application of noise over a large number of distributed models. These issues should be overcome by