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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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threatened species and ecosystems, and to control invasive species and diseases. This requires a step-change in the data and methods used to monitor and predict organism behaviours and ultimately shifts in
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provide high-quality operational and administrative support, oversee service contracts, ensure compliance with quality and safety standards, and contribute to continuous improvement initiatives. This is an
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, collaborative community Be surrounded by extraordinary ideas - and the people who discover them The Opportunity Monash University is seeking a Manager - Server & Virtualisation Operations to drive the strategic
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Project Background & Motivation Active learning (AL) mitigates the heavy annotation costs of deep learning by strategically querying the most informative unlabeled samples. However, traditional AL
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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on their location or textual information. The aim of this project is to build a next-generation navigation system by addressing limitations in the current systems – such as allowing more meaningful distance measures
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Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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new approaches for a playful human-computer integration future. For more information see http://exertiongameslab.org The result will be a thesis in the field of interaction design.