320 data "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" 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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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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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
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techniques in bacterial genomics, including both short- (Illumina) and long-read sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes
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Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation
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of the Australian Higher Education System,” which aims to analyse the Australian tertiary system using linked ABS and Department of Education data to inform policy on graduate earnings, peer effects, and field
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back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
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to studying learners’ engagement with feedback have relied heavily on self-reported data. While informative, self-reported data can be susceptible to bias, poor memory, and incorrect self-assessment
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nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make informed food choices and reduce diet-related emissions. Required knowledge Data analytics and software