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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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for tomagraphic imaging in tissue Neural network correction of distortions in acoustic transducers web page For further details or alternative project arrangements, please contact: alexis.bishop@monash.edu.
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learning to guide the self-aware learning and network formation. As such this expected to be a purely mathematical and computational project. To do this project you would need to apply for a Monash
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The energy transition to net zero is in full swing! We at Monash University's Faculty of Information Technology (FIT) are in the unique position that we support the transition across an immensely
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networks so that they can accurately identify anomalies in the presence of concept drift. We would like to consider different types of changes in graph structures, such as emergence/deletion of new nodes
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, identifying molecular disease signatures and matching them with the most effective therapeutic interventions are essential. The Hudson‐Monash Paediatric Precision Medicine (HMPPM) Program aims to develop and
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events with the GOTO telescope network. Projects focussing on thermonuclear bursts will involve analysis of new and archival data from satellite-based X-ray telescopes, and running numerical models
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Modern map-based systems and location-based services rely heavily on the ability to efficiently provide navigation services and the capability to search points of interests (POIs) based on their location or textual information. The aim of this project is to build a next-generation navigation...
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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traditional optimisation methods and modern deep learning techniques. Mixed integer linear programming is a successful discrete optimisation methodology but it is incompatible with deep neural networks, which