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We have a range of potential research projects on offer in partnership with VIFM - https://www.vifm.org/ - looking at ML techniques in predicting forensic diagnoses / image analysis, across
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design, contributing to our understanding of experiencing the human body as play. More information at http://exertiongameslab.org
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sensing equipment and theoretical work around play. For more information see http://exertiongameslab.org
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information see http://exertiongameslab.org
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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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contrastive self-supervised learning task to learn from massive amounts of EEG data. Frontiers in human neuroscience. [2] https://www.emotiv.com
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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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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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this website but submit the Expression of Interest via the Monash Data Futures Institute Application Link: https://www.monash.edu/data-futures-institute/study/phd-scholarship/mobility-data-driven-planning