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identifying real households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models
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. To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and
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therapy resistance has already been observed in both patients and pre-clinical mouse models, highlighting the urgent need to define the mechanisms that drive resistance and to identify rational combination
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complex interactions between geology, fluid flow and water–rock reactions. Changes in pressure, temperature and fluid chemistry during production and reinjection can promote mineral dissolution and
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developing representations, using neuroimaging with fMRI and OPM-MEG, and to model the developmental process using computational models from AI. The project aims to better understand infant cognition, develop
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currently looking for a methodologist to complement our team. The project focuses on building principled models that combine genetic association data (GWAS), molecular QTLs (eQTLs and pQTLs), (tissue-specific
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disorder with debilitating effects on a patient's quality of life. Because current medications do not work well, we need to study its molecular cause to develop new therapeutics. Recent research identified a
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STLO) (https://eng-stlo.rennes.hub.inrae.fr/ ). This work is part of the “Predicting the browning of dairy powders by kinetic modeling of Maillard reaction and caramelization during drying and storage
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lifespan of geothermal systems depend on the subsurface distribution of heat, fluid flow pathways, and recharge mechanisms and fluid chemical composition. These factors control the precipitation, dissolution
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of mill and production operations. The scientific challenge will be to use the model and machine learning alongside live mill data (temperature, rolling loads etc) to reverse engineer the current