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Field
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workshops, public talks, and community engagement activities Conduct a meta-study on collaborative transdisciplinary PhD cohort Co-develop energy literacy and communication projects Collaborate with academic
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-study on collaborative transdisciplinary PhD cohort Co-develop energy literacy and communication projects Collaborate with academic, government, and community partners to co-create research directions
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-life environments. The Role: As Research Fellow on the COG-MHEAR project, you will have the opportunity to use your strong background in deep neural networks and multimodal hearing-aid signal processing
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in advanced signal processing techniques and good understanding of emerging machine learning methodologies used in NDE. You will work in close collaboration with project partners at the University
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for cellular signaling Prepare manuscripts for peer-reviewed publication Present research findings at national conferences Collaborate with interdisciplinary research team Required Qualifications* PhD in
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applications from the Aboriginal and/or Torres Strait Isla n der community. At RMIT, we are committed to supporting adjustments throughout the recruitment and selection process, as well as during employment. We
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Proficiency in analytics, AI, and modern data science tools Excellent problem-solving, project coordination and communication skills A PhD or equivalent research experience (preferred) Experience with clinical
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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(or equivalent) on their master’s degree in order to be admitted to the PhD programme. The grade point average also applies to 300 ECTS points integrated five-year master’s degrees, in which all courses are to be
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areas: Time-series analytics or forecasting Natural language processing (especially question answering or language grounding) Multimodal learning (e.g., combining text with temporal or numerical data