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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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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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., feature engineering, spatiotemporal modeling, Bayesian calibration, ensemble methods) to improve prediction accuracy and uncertainty quantification. Disseminate research findings through presentations
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supervisor(s). The report models, performance evaluation criteria, and the grant contract model are those approved under the University of Coimbra's Research Grant Regulations. Where to apply Website https
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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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Overall, Purpose of the Job The main tasks are to advance the use of high-fidelity modelling to improve understanding of the characteristics of the turbulent wakes that develop downstream of large
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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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-omics analysis, systems modeling, and predictive simulation of host-microbiome-driven disease processes Translation of microbiome and systems-level discoveries into clinically actionable biomarkers
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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