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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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qualitative research. This project aims to develop a scalable EEG foundation model capable of learning general-purpose representations of brain activity that can support diverse neurotechnology and multimodal
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cross-disciplinary and focussed on translating technological advances in biomedical engineering towards improving outcomes for cancer patients. PhD students within the group are supported by postdocs
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using established rodent models. The role will involve designing and conducting sophisticated in vivo experiments, with a particular emphasis on wireless EEG/EMG telemetry recordings, behavioural
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behavioural testing, EEG and structural MRI. Working alongside researchers from diverse scientific backgrounds, you will apply your expertise in coding, data analysis and research operations to support
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Seizure prediction algorithms will be developed using the one-of-a-kind ultra-long-term human intracranial EEG dataset obtained from the Neurovista Corporation clinical trial of their Seizure
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This project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis
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This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and neural field modelling of the electroencephalographic (EEG) signal
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biology, molecular biology and animal models to investigate how these hybrid cells favour carcinogenesis. Where to apply Website https://app.smartsheet.au/b/form/019eedeef91978fdbc2b67718479eac6?project_id
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staff to supervise research and teaching, 51 fixed term staff and postdocs, 97 postgraduate students and 2,288 equivalent full-time student-load undergraduate students. To learn more about the School