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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
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—these approaches can recover unmeasured near-wall structures, improve subgrid-scale modelling, and enhance predictive accuracy. Possible project directions include: 1. Reconstructing near-wall velocity fields from
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Are you a researcher driven to understand and predict the fundamental mechanisms limiting lithium-ion battery performance? We are recruiting a Research Associate in Lithium-Ion Battery Modelling
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of data analytics and mathematical modeling to predict clinically relevant biological outcomes using in vitro engineered tissue systems and in vivo models and will play a central role in the development
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RAP opportunity at National Institute of Standards and Technology NIST Modeling Complex Microstructures Location Information Technology Laboratory, Applied and Computational Mathematics Division
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to correct or account for these biases, and build predictive models that simulate biological responses to in silico perturbations such as genetic or pharmacological interventions. The project aims to advance
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dataset generation technique to optimize the training of neural networks (NNs) for seismic data prediction. The use of neural networks to predict seismic velocity models has shown increasingly accurate and
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. Furthermore, a novel predictive algorithm of School-age neuropsychological outcome will be developed combining radiomic model of brain development, with qualitative neonatal MRI findings. Achievement
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to accelerate the path to certification. More details on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models
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-funded DECIPHER-M consortium (9 partners, €9M), we are building multimodal foundation models that integrate imaging, text, and structured clinical data to predict metastasis risk and identify tumor origin