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drought early warning and monitoring system for large-scale river basins. The project will explore both data-driven and model-based approaches for drought predictions, paving the way for a continental high
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analysis utilizing methods rooted in artificial intelligence (i.e. machine learning and deep learning). The analysis will be the basis for developing a predictive model to help select the most optimal method
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that predict genetic cancer risk and help explain these risks to people in a clear and safe way. One part of the project builds AI models that can predict the risk of several cancers at once and explain how
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prediction outputs. The first PhD will work on data fusion, feature extraction, and model development ranging from baseline approaches (e.g., gradient boosting) to deep learning architectures. The work also
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PhD researcher in AI:GENOMIX, you will contribute to next-generation models that rethink polygenic prediction and support the future of precision medicine. AI:X is an ambitious initiative at Aalborg
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using thermal fertility limits as well as microclimate modelling for predicting distributions and seasonal population dynamics. As such the position will include a combination of ecological modelling and
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of wind turbines. Despite remarkable progress in structural health monitoring boosted by AI, purely data-driven models have no physical interpretability and poor generalization capabilities. Thus
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to contribute to the groups ongoing work on integrating environmental issues into macroeconomic models with the purpose of providing an assessment of the financial stability and physical risks given
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issues. It is therefore essential to be able to predict and control the moisture state in construction materials. Traditional material characterization methods, standards and models for hygrothermal
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, large-scale foundation models will be developed and trained on the Aalborg Supercomputer (TAAURUS), facilitating advanced ECG representation learning and prediction of acute coronary events in