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learning, materials characterisation or computational materials science. Previous experience with machine learning, computer vision, graph neural networks, Python, SEM/EBSD, XRD, image analysis
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testing and assessment of device-assisted aerosol delivery under clinically relevant operating conditions. Data processing, image analysis, uncertainty assessment and experimental interpretation using
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, computer vision, federated learning, foundation models, adaptation techniques, multimodal learning, longitudinal image analysis or related areas, evidenced through coursework, research projects
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: · Systematic review and meta-analysis · Evidence synthesis and critical appraisal · GRADE guideline development methodology · Living evidence methods · Clinical epidemiology
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social media and online platforms. The project will use natural language processing (NLP), large language models (LLMs), and network analysis to identify coordinated harassment, anti-gender equality
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AI infrastructure. The Opportunity This is an unprecedented opportunity for an outstanding data science and AI PhD candidate interested in brain data analysis and AI, supervised by Dr Mahsa Salehi
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with
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of discourse, identity and meaning-making within online environments. Approaches such as semi-structured interviews, digital ethnography or social media data collection (including platform-based content analysis
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Expanding access to digital health The successful candidate will receive training in applied microeconometrics, health economics, environmental economics, discrete choice experiments, administrative data
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, scientific computing, automated data analysis, or computational modelling. Candidate Requirements Applicants will be considered provided they fulfil the criteria for PhD admission at Monash University. Details