296 data-"https:"-"https:"-"https:"-"https:"-"https:"-"P" positions at Monash University
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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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promises to enhance feedback practice by providing real-time data and insights into learning behaviour and outcomes, so as to inform educational interventions. However, the feedback loop remains open without
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projects that involve data analysis, the application of artificial intelligence, the development of new detection techniques, and the exploration of new experimental methods through collaboration with our
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extract events and mine knowledge from existing unstructured/structured data, and exploit the knowledge via neuro-symbolic reasoning for crime prevention (eg -sexual assaults), especially when there is no
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challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up
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are supported by quantum mechanical theoretical formalisms. Our fundamental findings yield promise for future applications in electronics, optoelectronics, spintronics, information processing and storage, sensing
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This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation
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to develop your leadership, public speaking and teamwork skills. Applications 2 Aug - 8 Oct 2021 Total scholarship value Up to $24,000 Number offered Up to two per year See details Bachelor of Applied Data
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required to submit A cover letter that includes how you meet the criteria list above regarding current course and WAM information. A 1000-word essay to arts-mjlec@monash.edu explaining why you wish to become
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the research, development, deployment ,and use of AI systems in law enforcement (LE). A major focus is expected to concern the acquisition, use, sharing and governance of data for AI in this context. The Faculty