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activity, sleep and health behaviour change Project Description: Digital behaviour change tools reach large populations cheaply, but most deliver generic advice and lose their users within weeks. Large
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datasets with different configurations (e.g., number of channels, sampling frequency and resolution). To leverage large-scale self-supervised learning to train models on unlabeled EEG data, reducing reliance
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social media and online platforms. The project will use natural language processing (NLP), large language models (LLMs), and network analysis to identify coordinated harassment, anti-gender equality
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I specialise in the numerical modelling of high-energy particle collisions , such as those occurring at the Large Hadron Collider. Accordingly, most projects I offer straddle the intersection
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infrastructure, public safety and smart environments. The successful candidate will be supervised by Dr Deval Mehta within the Department of Data Science & AI, Faculty of Information Technology, Monash University
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is carried out within the LHCb collaboration that runs one of the four large experiments at the Large Hadron Collider at CERN as well as towards future collider developments. I supervise a number of
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someone with strong quantitative skills and an interest in using data to answer policy relevant research questions. The successful applicant will work with large administrative and survey datasets and apply
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. Analysis of this data will employ both qualitative and quantitative methods. Work on WP-2 will suit someone with an interest and aptitude for coding administrative data using large language models. It will
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understand where stars form, how stellar feedback changes their surroundings, and how matter moves through galaxies over time shaping them. Most of my research uses large astronomical data sets across
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understand our place in the cosmos. I am a member of most large stellar spectroscopic surveys (e.g., Gaia, SDSS-V, 4MOST, GALAH, Gaia-ESO), providing access to pan-optic data across all visible and infrared