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opportunity to work in a top-tier interdisciplinary setting. This is what you will do You will develop predictive computational models to capture the formation and heterogeneous structure of microthrombi, with
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at developing and applying multiscale numerical models for the thermal-hydraulic safety analysis of advanced nuclear reactors, with a focus on the prediction of Critical Heat Flux (CHF) in Small Modular Reactors
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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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Institut de Recherche en Génie Civil et Mécanique (GeM) | Saint Nazaire, Pays de la Loire | France | 10 days ago
or random fields or, propagated through the predictive models to improve their robustness. The measurements may also be compared with other mechanical characterisation techniques, including in situ approaches
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meteorological data; compilation of data on the incidence of relevant vineyard pests and diseases; study of environmental conditions favorable to their development. 2) Development of the predictive model (Months 3
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and implement multimodal retrieval with re-rankers for robust profile selection. Design and train advanced AI models for digital twin: 3D model learning, prediction models from imaging and molecular
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of descriptive and predictive mathematical models. Examples of current and relevant problems in modern society that can be treated using such methodologies are ensuring the efficiency of industrial and
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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modeling to create a predictive tool that spans orders of magnitude in length and time. Hands-On Numerical Modeling: Implement your model in a custom-made data analysis tool that uses advanced optimization
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modes, effects, and criticality requires deep domain knowledge and careful analysis. Collecting High-Quality Sensor Data. Simulating Realistic Fault Conditions. Developing Reliable Fault Prediction Models