354 data "https:" "https:" "https:" "https:" "Dr" "L2CM" positions at Monash University
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will be developed to identify and reason over causal relationships among all associations from the data in literature. As the number of causal relationships is usually much smaller than
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to avoid system bottlenecks, and ensuring low-latency performance. Energy-Efficient Operations with Carbon-Aware Scheduling: Example: For non-urgent data processing, SmartScaleSys (S3) could prioritize tasks
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Director, Opportunity Tech Lab; Chair of the Steering Committee, Monash Business Behavioural Laboratory) Dr Mor Vered (Department of Data Science & AI, Faculty of IT) Dr Estelle Wallingford (Department
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the observer. Active Goal Recognition extends Goal Recognition by also assigning the data collection task to the observer. This Ph.D. project will provide a unified probabilistic and decision-theoretic
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checks required for the role, as determined by the University. Enquiries: Dr Jane Holden, Chief Operating Officer, Australian Research Council (ARC) Centre of Excellence for the Elimination of Violence
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by the University. Enquiries: Dr Jane Holden, Chief Operating Officer, Australian Research Council (ARC) Centre of Excellence for the Elimination of Violence against Women (CEVAW), jane.holden
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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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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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Privacy-Enhancing Technologies (PETs) are a set of cryptographic tools that allow information processing in a privacy-respecting manner. As an example, imagine we have a user, say Alice, who wants
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to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from