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. In this way, flood impacts can also be described probabilistically and later added to the model of Pillar 1; Translating the current scenario sets into future climate states (using climate scenario as
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defences, and account for uncertainty in breach growth. In this way, flood impacts can also be described probabilistically and later added to the model of Pillar 1; Translating the current scenario sets
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deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as PyTorch
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procedures. The project brings together cardiovascular imaging, generative AI, and computational modeling to develop probabilistic digital twins of coronary arteries. The goal is to create patient-specific
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selection and validation, supervised and unsupervised learning, optimization techniques, (deep) neural networks, probabilistic methods and statistics, data visualization, natural language processing
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with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as
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Renovation Decision-Making”. The PhD position is part of the Horizon Europe CLIMABUILD project. The candidate will develop probabilistic life-cycle modelling methods to support stakeholders in identifying
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probabilistic digital twins of coronary arteries. The goal is to create patient-specific models that combine anatomical and hemodynamic information derived from routine clinical imaging. By providing quantitative
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notions of resilience have to be developed along with algorithms to check resilience of machine learning models. Research is conducted in the fields of automated reasoning, probabilistic verification, and