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interpersonal and communication skills, with the ability to negotiate and build consensus across all organizational levels. About Monash University At Monash , work feels different. There’s a sense of belonging
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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their performance both empirically and through controlled user studies. Required knowledge Strong background in computer science in general Familiarity and understanding of basic principles underlying automated
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In collaboration with people from Monash materials engineering, neuroscience and biochemistry we are developing living AI networks where neurons in a dish are grown to form biological neural
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that are constructed in a way that is inspired by what we know about self-awareness circuits in the brain and the field of self-aware computing. The project will advanced state of the art AI for NLP or vision or both
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PatchSentinel-X: Transformer-Based Security Patch Intelligence for Vulnerability Lifecycle Assurance
++, Java, or Python code, Basic static analysis tools such as Semgrep, CodeQL, or SonarQube Nice to Have CodeBERT, CodeT5, or GraphCodeBERT, Program analysis, Data-flow and control-flow understanding
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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. This will be achieved through frequency domain and time domain state and parameter estimation...
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In many branches of science (e.g., Artificial Intelligence, Engineering etc.), the modelling of the problem is done through the use of functions (e.g., f(x) = y). On a very high-level, we can think
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AI is now trending, and impacting diverse application domains beyond IT, from education (chatGPT) to natural sciences (protein analysis) to social media. This PhD research focuses on the fusing AI
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potential remedial approaches - will be explored in this research program and they include (as examples): variability in staining outcomes across different stains and different sites (even within a given