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of techniques it offers, making ML seem an excellent tool for any task that involves building a model from data. Nevertheless, ML makes an implicit overarching assumption that severely limits its applicability
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Machine Learning without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research
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-reported data. While informative, self-reported data can be susceptible to bias, poor memory, and incorrect self-assessment. This project will complement this self-reported measurement of feedback literacy
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information on our practice-based PhD program, please see: https://sensilab.monash.edu/work-with-us/practice-based-phd/
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/scholarships/scholarship-policy-and-procedures The Opportunity This PhD scholarship forms part of a collaborative research program on digital transformation, data practices, and everyday life in Indonesia
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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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Nowadays more and more intelligence software solutions emerge in our daily life, for example the face recognition, smart voice assitants, and autonomous vehicle. As a type of data-driven solutions
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trends that started earlier in other time series. Part of what will be done will be to identify sufficiently similar time series - and to pool (or combine) relevant data. One of the approaches that will be
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combination of multi-wavelength observational data with sophisticated simulations. I am a member of various collaborations, including Australia's OzGrav Centre of Excellence for Gravitational-wave Discovery
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months, and link survey data to national administrative data systems for longer-term follow-up. The successful candidate will play a leading role in all aspects of the cohort study, including its design