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. The models will be used to predict dynamic responses to stressors, sleep disruptions, and diagnostic tests, as well as the long-term changes that occur during disease. A key challenge of your work will be
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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties
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(iii) the integration of enzymatic ex vivo models with advanced constitutive and damage laws. In the longer term, this work will contribute to a predictive framework of menopausal tissue fragility, as a
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, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating
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and Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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close coordination with project partners, the recruited researcher will conduct experiments to determine the extent to which neural models, now at the heart of many approaches to Natural
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supports tasks such as predictive modeling, anomaly detection, and synthetic data generation. The models developed are expected to exploit metadata to guide and condition image analysis outputs. By
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. The models will be used to predict dynamic responses to stressors, sleep disruptions, and diagnostic tests, as well as the long-term changes that occur during disease. A key challenge of your work will be
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of mold free shelf-life predictive models, determining the number of variables as well that need to be recorded to be able to train the model; (ii) design and development of a model to predict mold growth