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, you will explore how data-driven models capturing the state-of-health and degradation can be integrated in the battery model. You will develop these machine learning-based proxies together with a
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on the participation in multiple consecutive short-term electricity markets and congestion management. To address this question, you will develop state-of-the-art model predictive control tools to guide market
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Research Project“ Transforming Cardiac Research: Visual Exploration and AI Prediction Modeling of Real-Life, Multi-Modal Data” as a PhD-Position in machine learning. You will work alongside leading experts
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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evaluate them alongside newly developed approaches. Integrating methods such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary
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/SinglePageApplicationForm.aspx… Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Professional skills Experience in: Reinforcement Learning (RL), Model Predictive Control (MPC
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: estimation theory (state observers, parameter identification), adaptive control, model predictive control, modelling and simulation, machine learning. Practical experience with the above. Personal
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scans and use advanced data-driven methods, including artificial intelligence (AI) and machine learning to improve outcome prediction and patient stratification. deepen our understanding of the etiology
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches