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Conservation management needs reliable information about how many animals a population holds and whether that number is changing over time. Yet for most threatened species such estimates are often
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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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estimate the reliability of its own perception and reasoning, then use that information to adjust how it processes a task. For example, a straightforward scene may be handled by a lightweight model, while an
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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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Duration: 3.5-year PhD scholarship, subject to Monash University scholarship conditions Remuneration: The successful applicant will receive a Research Living Allowance, at current value of $ 37,145 AUD per
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biology, simulation, data visualisation, bulk / single-cell / spatial RNA-sequencing analysis. Skills Terms programming; R; Python; Statistical methods; machine learning; differential expression; bulk
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compromised services, and restore safe system states. A key focus will be on safe autonomy. The system should not blindly execute actions. Instead, it should verify its own decisions, estimate risk, explain its
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measurable privacy-risk score that estimates how easily a user can be psychologically profiled from posts, comments, likes, or behavioural patterns. LLM-Based Profiling Attack Benchmark A benchmark that tests
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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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that explicitly account for drift and open-set contamination. O2: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal