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There is an opportunity to prototype interactive muscle memory systems and study their use in order to understand what designers can learn from remembering activities that involve the active human
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of novel data sources and agent-based models for the Australian cane toad invasion. The successful candidate will use this case study as a foundation for integrating multi-paradigm modelling approaches and
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system to unlock important information from unstructured data sources including X-ray images, surgical and radiology text reports. We will compare prediction models based on existing, routinely collected
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development lifecycle greatly improves its quality and productivity. Here calls for a systematic development lifecycle for the DL systems. Due to the fundamentally different programming paradigm and logic
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nucleosynthesis in violent episodes suffered by ancient stars" (with Dr Carolyn Doherty) "Applying 3D stellar hydrodynamics findings to 1D stellar codes: Improving the modelling of convection in stars" web page
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Linguistics (ACL), 2019 [2] Learning to Actively Learn Neural Machine Translation [code] Ming Liu, Wray Buntine, Gholamreza Haffari In Proceedings of CoNLL, 2018. [3] Learning How to Actively Learn: A Deep
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programming. An ability to code in C, C++ or Rust is also necessary. Candidates with experience or interest in column generation, cutting planes, polyhedral geometry and graph theory are especially invited
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narrative to communicate insights (instead of just plotting data). The aim for this PhD is to research, prototype and evaluate approaches to increase the explanatory effectiveness of the visualisations
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as child exploitation material). It will involve the use of deep neural networks for image, video, audio, social network, and/or text classification. The Faculty of Information Technology has a mission
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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