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to reproducible research, critical analysis, and publication Experience with deep learning, audio analysis, or affective computing is advantageous but not mandatory.
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challenge is to create disentangled representations for paralinguistic information and the content of speech. Herein, a component of such a disentangled representation contains only necessary information
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Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but
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resources to avoid downtime, adjusting dynamically as traffic fluctuates. For researchers and students, this component focuses on developing ML models to predict resource needs, improving load distribution
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. To Apply For instructions on how to apply, please refer to 'How to apply for Monash Jobs '. Please ensure your cover letter considers the most important elements of the Key Selection Criteria of the position
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, custom ringtones, specific genres). Required knowledge Python programming Machine learning background Audio analysis
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This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods
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The United Nations Development Programme has identified access to information as an essential element to support poverty eradication. People living in poverty are often unable to access information
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Nanotechnology (IF=39.21), Brief in Bioinformatics (IF=11.62), Hypertension (IF=10.19), IEEE Transactions on Pattern Analysis and Machine Intelligence (IF=24.31), IEEE Transactions on Medical Imaging (TMI
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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