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biopsies and develop and validate predictive models that support and enhance personalized care. Your tasks include: Designing and conducting scientific research, with a focus on machine-learning based
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, computational modelling, and machine learning, and is well-suited for candidates eager to develop computational frameworks for next-generation structural dynamics and nanomechanical technologies. The vacant
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machine learning to improve outcome prediction and patient stratification. deepen our understanding of the etiology, clustering, and diagnostics of cardiovascular diseases in critically ill patients
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for uncovering nonlinearities and dissipation mechanisms, and design strategies for exploiting them. The project sits at the interface of nonlinear mechanics, computational modelling, and machine learning, and is
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incomprehensible model parameters that have been learned from data. For instance, why does a machine learning model predict that it is unsafe to discharge a certain patient from the intensive care? Or which
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learning, and systems engineering. Where to apply Website https://www.academictransfer.com/en/jobs/354917/phd-postdoc-on-model-predictive… Requirements Specific Requirements We are looking for talented
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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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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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