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• Strong quantitative and programming skills; experience with seismic data analysis or numerical modelling is highly desirable• Excellent written and verbal communication skills• Ability to work
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Vision and Edge Computing'. PhD candidates involved in this project will be trained in the emerging field of smart infrastructure, which is critical for Australian society in the coming decade
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various methodologies and methods to critically assess and/or propose (re)designs of digital technologies. These research projects will examine technology design and use, focusing on the intersections
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practical lab or workshop experience, especially if you’ve used tools like SEM, profilometers, or hardness testers to analyse materials. A knack for solving problems and thinking critically, with
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analysis and data processing. Strong programming skills in R (preferable) and/or Python, and experience or interest in weather prediction or climate models. Knowledge of machine learning, AI techniques, and
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conceptual and methodological framework to address this emergent more-than-human politics. A thoroughgoing critical analysis of frameworks of environmental governance is needed, alongside the development
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relevance across various surgical procedures and patient groups. Objectives 1. Algorithm Development: (a) Design and implement a deep learning algorithm for CT scan analysis. (b) Train, validate, and test the
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interaction and human motion analysis Prior knowledge of machine learning/deep learning applied to motion analysis (e.g., relevant courses and research experience) would be an advantage IELTS score of 6.5
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personal experience, but typically a 2XE internship is centred on advancing data analysis and developing tools for deploying transformative technologies within the renewable and smart energy sectors
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. Policy support is critical to accelerate their adoption but has faced setbacks and delays in Australia due to political resistance and low social acceptance. A key problem is that the models used