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Field
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sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and
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these models in real tissue context, revealing how spatially organized cell communities rewire under genetic perturbation. Key challenges Use and improve single-cell foundation models to predict SNP-driven
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. Process simulation software is being developed for virtual optimization of tool design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies
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sources and probabilistic reliability analysis to predict both current and future safety levels. This project contributes to designing future standards and safer vertical transport. Information
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improve prognostication in critically ill patients by integrating cardiovascular risk factors into existing ICU prediction models. by combining cutting-edge data science with advanced clinical expertise and
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to monitor patients’ health condition continuously and accurately after surgery to measure and evaluate patients’ recovery progress, timely detect and even predict clinical adverse events like delirium
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of Technology. The mission of the Dynamics and Control Section is to perform research and train next-generation students on the topic of understanding and predicting the dynamics of complex engineering systems in
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explore how artificial intelligence can be used to predict the response to therapy in cases of acute rejection of the kidney graft. Using machine-learning techniques, you will assess kidney transplant
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analyzes longitudinal data on participation. Examples of questions that will be answered are: How can we disentangle age and cohort effects in order to predict the future course of the classical music
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. The principal goal of the PhD project is to develop component models with a greater physical accuracy and predictive capability by employing state-of-the-art methods for the following two modelling approaches