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details can be found at https://www.net-zero-fibe-cdt.eng.cam.ac.uk/ The project is funded in collaboration with Ramboll and Buro Happold who work across diverse projects with key clients focused
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 27 days ago
associated. with neurodegeneration or ischaemic heart disease. Predicting optimised regenerative response. Developing and validating predictive biomarkers, personalized interventions, and AI-driven models with
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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. Further Information Working with University of Malta and associate partners at the Institut de la Recherche du Québec, the RSOs will be involved in developing new systems for modelling and predicting
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in defining comprehensive research objectives and activities including applying analytical techniques to help predict outcomes, creating models and identifying trends. Conduct thorough checks of data
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 5 hours ago
. Description: Ames scientists are actively involved in theoretical computation of extrasolar planet atmospheres, predicting exoplanet spectra, conducting and interpreting exoplanet observations, planning future
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the freshwater budget. It is essential to comprehend the various components of sea level change in order to enhance our capacity to predict and adapt to future sea level changes. It is expected that research
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supplying high-quality data needed to validate and refine the next generation of predictive numerical models. A key innovation in this research will be the use of transparent soil analogues of sand and clay
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any attempt to model and predict network behavior accurately particularly challenging. While simulations remain an essential tool for performance design and evaluation, traditional models often fail
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models trained on the Year 1 dataset to predict promising compositions. The third year focuses on application and physical metallurgy insights, where the student will apply the refined methodology to a