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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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questions for both Biogeography and Quaternary Palaeoecology, and the answers provide the basis for predictions of ecosystem and species response to future climatic change. We are looking for PhD candidates
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, for the project “PREDICT-OAE project (From data to PREDICTion: modelling temporal ecological–biogeochemical links under Ocean Alkalinity Enhancement”, financed by internal funds from CIÊNCIAS and FCiências.ID
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predictive control, optimization-based decision frameworks, and data-driven performance modelling. The overall goal is to develop computational methods that enable efficient and intelligent operation of wind
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), multimodal vision and language models, and Large Language Models. Please find prior work here: (Google Scholar: https://scholar.google.com/citations?hl=en&user=oEifmSgAAAAJ&view_op=list_works&sortby=pubdate
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modeling to create a predictive tool that spans orders of magnitude in length and time. Hands-On Numerical Modeling: Implement your model in a custom-made data analysis tool that uses advanced optimization
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to develop an aeromedical dispatch management software as a technology hub that provides data-driven prediction model and an automated dynamic decision model. The successful candidate will be responsible
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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department