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drought early warning and monitoring system for large-scale river basins. The project will explore both data-driven and model-based approaches for drought predictions, paving the way for a continental high
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behaviour for system monitoring, state estimation, and performance prediction. Build computationally efficient models suitable for monitoring, performance prediction, optimization, and control, and evaluate
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Human-Robot Interaction in industrial settings, in terms of monitoring psychophysiological states, predicting fatigue and posture risks, and detecting cognitive strain to provide timely cognitive support
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in complex biological networks e.g. food webs, forming the foundation of natural ecosystems. Yet, we lack the tools to predict how these networks change in time and space. This is especially critical
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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statistical modeling with clinical insight, aiming to improve risk prediction and inform sex-specific prevention strategies in atrial fibrillation patients. The research will be conducted in close collaboration
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for efficiency monitoring and fault detection, combining sensor data, system layout knowledge, and physical principles to extract spatial-temporal features and predict equipment behaviour; 2) Statistical anomaly
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methods (e.g., coding in Python/R, working with APIs, scraping data, building or applying models). • Can bridge theoretical insight with concrete technical implementation and empirical analysis. • Is