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for monitoring and controlling the brain with medical devices and imaging brain activity in new and important ways. Required knowledge Statistical signal processing, Statistical Inference, Machine learning, Deep
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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techniques. It may also include hardware development of wearable assistive devices that use audio and haptic feedback. Required knowledge Image processing Computer vision Deep learning Programming (Python, C
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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"A picture is worth a thousands words"... or so the saying goes. How much information can we extract from an image of an insect on a flower? What species is the insect? What species is the flower
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. The latest advanced techniques in machine learning and computer vision for image content analysis will be applied to generate data for dynamic species distribution models. This data will in turn be used
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the simple equation that more training data = better performance. Learning—in particular, the advanced deep learning methods, like BERT for NLP and ResNet for image processing—often require thousands
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development process for DL, covering requirement analysis, data collection and labeling, data cleaning, network design, training, testing, and operation. Required knowledge deep learning, natural
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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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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