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dynamic models to study ecosystem stability, resilience, and the possibility of irreversible change; investigating how biodiversity and soil conditions jointly determine whether restored ecosystems can
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groundwater flow models. Current groundwater flow models often exhibit important uncertainties related to input parameters and boundary conditions. Reducing this uncertainty is difficult if these models
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drought resilience and adaptation. Current groundwater flow models, however, often exhibit important uncertainties related to input parameters and boundary conditions. Reducing this uncertainty is difficult
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learning models that integrate: Air pollution, weather, noise, green space, urban form; Socioeconomic and demographic data; Hospital admissions and mortality records; Develop physics-guided and graph-based
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role Conduct research on data quality and long-term reliability in smart sensor systems for industrial monitoring Develop methods, models, and computational tools for sensor data validation, anomaly
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project investigating the role of epigenetic changes (DNA methylation) in cardiomyocytes during ageing and atrial fibrillation. The research involves human heart tissue samples as well as mouse models
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, the PhD student at ETRO will contribute to the core of the AI pipeline by designing and validating generative models (e.g. latent diffusion-, autoencoder-, and GAN-based approaches) that can infer
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Environmental science » Global change Computer science » Programming Computer science » Modelling tools Computer science » Other Computer science » 3 D modelling Researcher Profile First Stage Researcher (R1
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exposomic and health data into actionable risk predictions and feedback systems. You will: Design spatio-temporal machine learning models that integrate: Air pollution, weather, noise, green space, urban form
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continuous surgical training for clinical staff, military personnel, and even civilians. Unfortunately, current training methods, such as synthetic models (patches and phantoms), animal models, and virtual