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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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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
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, and c) predicting new phenomena and discovering improved materials for applications. My efforts in this area use a variety of modeling approaches to answer questions on materials systems of interest
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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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