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. This includes exploring the use of digital twins for bioreactors and deploying AI driven predictive models to improve optimisation, consistency and overall yield. The main focus for this role is to work with the
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short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning
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for our environment? Will the streets ever by quiet again? This PhD will give you the opportunity to build models that predict this! Job description In 2023, global drone shipments reached 1 million
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have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models for Alzheimerâ™s, Parkinson, and Dystonia as well as to identify novel
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models ignore this exposome. In BEE, we will build explainable, physics-guided, GeoAI-driven models that: Predict acute and chronic NCD risks at the population scale Identify vulnerable neighbourhoods and
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Wetsus - European centre of excellence for sustainable water technology | Netherlands | about 1 month ago
of digested humanure on soil. For the 100 most commonly prescribed pharmaceuticals in the Netherlands, their excretion profiles and their physico-chemical properties will be used to predict their persistance in
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: Portugal Website: https://www.lnec.pt Email: recrutamento@lnec.pt The scientific research, technological development, management and communication of science and technology activities shall be carried out
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transfer This research combines advanced numerical simulation and artificial intelligence to develop predictive models for high-temperature multiphase flows, with specific relevance to steel casting
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porous materials Develop novel machine learning model for predicting gas adsorption behavior Investigate molecular transport and separation mechanisms for membrane process Publish journal articles and
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to be enrolled in a study programme leading to a PhD (or in a non-degree programme). - Have proven experience in physical and digital modeling to predict the structural performance of packaging, enabling