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, updated 2025/11/03, listed until 2026/05/16 04:59 AM UnitedKingdomTime) Position Description: Apply Position Description The Center for High Order Plasma Turbulence Modeling for Z-Pinch (HighZ: https
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on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and
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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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, with healthcare innovation and pharmaceutical industry partners. Our research on early prediction of dementia (https://www.bbc.co.uk/news/health-57934589 ) has the potential to deliver significant
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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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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 | 3 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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the High Temperature Gas-cooled Reactor (HTGR) as the most credible Advanced Modular Reactor (AMR) technology. Achieving improved performance requires accurate, high-fidelity modelling to reliably predict power
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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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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