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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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. Read more about CEBE (https://villumfonden.dk/en/nyhed/billion-kroner-research-grant-accelerate-green-transition-built-environment). The transition from CO2 producing building materials to renewable, CO2
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topics: a) introduction of highly efficient DGL models to reduce the energy impact and increase the sustainability of DGL models; b) increase the expressiveness of DGL models, obtaining better predictive
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porous materials Develop novel machine learning model for predicting gas adsorption behavior Investigate molecular transport and separation mechanisms for membrane process Publish journal articles and
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Research Infrastructure? No Offer Description Mission: Support the design, training and validation of temporal models aimed at detecting ecological patterns and predicting events such as the bloom of Oceanic
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attention to scientific rigor and interpretability Experience with XAI tools (SHAP, LIME, Integrated Gradients) to identify which features of the model are driving the predictions Clear written and verbal
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the University of Porto (FEUP), under the scientific supervision of Professor Alexandre Ferreira. Grant duration: Initial duration of 3 months, with the predicted starting date in May 2026, on an exclusive basis
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: Initial duration of 6 months, with the predicted starting date in April 2026, on an exclusive basis eventually renewable but never exceeding the project duration. If it is not possible to ensure
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months, with the predicted starting date in april 2026, on an exclusive basis eventually renewable but never exceeding the project duration. If it is not possible to ensure the duration of 6 months
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the integration of behavioural data with AI. The student will analyse eye movements, exploration patterns, and verbal reports to develop computational models that predict identification reliability