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Background in Machine Learning, Algorithms and Data Structures
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In this project, we will use machine learning methods to diagnose the health status of bee colonies and individual bees. Bee populations are threatened worldwide due to a number of factors
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distributions. We wish to represent the biological networks into proper formats, e.g., vector representations, so that existing machine learning algorithms (e.g., support vector machines) can readily be used
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inference and machine learning to develop subject specific mathematical models of the brain that can be used to infer brain states and monitor and image the brain. This work is centred around a
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for predictive analytics that incorporate modelling, machine learning, and data mining, we are building, analysing and modelling an individual’s baseline health profile against thousands (eventually millions
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information about behavioural patterns, but scoring this manually is time consuming. For this reason, machine learning solutions have been developed to automate behavioural prediction [5-12]. DeepLabCut [5] is
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. This work combines computational modelling and simulation with biological experiments that are analysed using cutting-edge computer vision techniques. We collaborate closely with Macquarie University where
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Insects are vital components of natural and agricultural ecosystems that interact with plants in complex ways. Computer simulations can help us understand these interactions to improve crop
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the mobility data and design optimisation and machine learning algorithms Validate the design on energy-transport simulation platforms Present and promote research findings in international conferences and
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engineers to produce the highest quality software systems with the lowest operational costs. To achieve this, this project will invent an end-to-end explainable AI platform that leverages advanced machine