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, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read here
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languages such as R, Python, Matlab. Familiarity with software development best practices (e.g., unit testing, version control). Familiarity with inferential statistics and machine learning. Expertise in
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Institute for Machine Learning – the largest computer vision and machine learning research group in Australia – and contribute to world-leading research projects at the Centre for Augmented Reasoning
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biology. The Snell Laboratory collaborates closely with AI and classical machine learning developers, and the selected candidate should have expertise or an interest in acquiring expertise in AI and
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype
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to your expertise. What do we require? A PhD degree (or equivalent qualification) in AI (e.g., machine learning, natural language processing or computer vision); A strong scientific track record, documented
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biology, epidimological data and AI-driven systems modeling. The successful candidate will develop and apply computational and machine learning approaches to decode the molecular and epigenetic mechanisms