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unavailable or imprecise, either because the species is cryptic and hard to detect by visual monitoring or because current methods struggle to separate individuals from one another. Passive acoustic monitoring
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anomalies in evolving graphs. In this research proposal, our aim is to explore the parallels of deep learning and anomaly detection in dynamic graphs. In particular we are interested to redesign deep neural
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. Machine-learning and deep-learning techniques will be developed and compared for cancer-risk prediction, classification and prognostic modelling. Explainable and Multimodal AI Explainable AI (XAI) methods
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., Boruta) and statistical imputation methods (e.g., KNN/Iterative Imputer) are applied to optimize variable efficiency and handle missing data. Ensemble algorithms—specifically Gradient Boosting, LightGBM
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computation is necessary before taking an action. This PhD project will address these challenges by developing trustworthy and resource-adaptive VLA models. A central question is whether an embodied agent can
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Centre for Health Economics, Monash Business School, Integrated PhD Program 2027 Fully Funded 4.5-Year PhD in Health Economics - Monash University (Melbourne, Australia) Job no.: 625101 Location
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. Selected methods can then be evaluated through real-world human-robot interaction using humanoid and mobile robotic platforms. Aim/outline The aim of this project is to develop multimodal world models
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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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data provides opportunities for advanced Machine Learning (ML) approaches. Research Aim This PhD research aims to develop advanced Machine Learning methods for prediction, risk stratification, clinical
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program through the analysis and interpretation of research data, preparation of manuscripts and conference presentations, and the development of research questions and ideas. It will also support the