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monitoring. • Develop numerical models to simulate the dynamic behavior of mooring and anchoring systems under different environmental conditions. • Analyze and optimize structural performance and predictive
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and observation models to reflect real-time changes in environmental conditions, enabling more accurate predictions of adaptation impacts and thereby supporting a better-informed, resilient decision
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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is to move beyond traditional “check-after” approaches and instead predict and prevent errors while the radiotherapy treatment is being delivered. The candidate will build upon this existing prototype
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies
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of whole plants at crop level. A central element is the plant’s 3D geometry, and models should predict plant growth, development, and yield as well as key physiological relationships across the whole plant
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, predictive modeling, and decision support. This role combines deep analytical expertise with business acumen to transform complex data into actionable insights that support strategic planning and financial
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better quality of life for patients and caregivers, and lower healthcare costs. The target is to define new intelligent computational models by reshaping risk prediction, diagnosis, and management of a
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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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experimental and computational datasets. The overarching objective of this work is to establish predictive, patient-specific models capable of forecasting clinical outcomes in breast reconstruction, thereby