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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in the prediction and modelling of extreme flood events? Do you want to understand how
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of an Experienced Researcher with 12,50% of the regular working time is to be filled as soon as possible, for the project Non-Gaussian self-similar processes : Enhancing mathematical tools and financial models
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-impact resource that improves mechanistic drug profiling and informs model selection across cancer research and drug discovery. In line with our and Uni Basel values (https://www.unibas.ch/en/Research
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of documentary evidence through their transposi-tion into the field of Digital Heritage, with the development of HTR models, the identification of data-mining processes and digital edition or TEI coding strategies
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projects. The position is responsible for conducting sponsored research in transportation safety, developing material and computer simulation models, developing vehicle and roadside hardware models, and
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to the understanding of physical mechanisms underlying these complex plasmas. The laboratory team has extensive experience in both experimental and modeling of this type of plasmas. Thus the candidate will have existing
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postdoctoral researcher to join the ERC Pandora project team. Your work will involve analyzing or designing models of surface processes to estimate their role in the formation of environmental refuge zones
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related to staff position within a Research Infrastructure? No Offer Description About the Role We are seeking to recruit a Research Fellow in AI for Climate Modelling to become a member of the School
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solutions, focusing on building robust data pipelines, creating efficient machine learning models, and integrating AI capabilities into existing systems to improve efficiency, accuracy, and service quality
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improving and extending existing modeling frameworks such as the Large Basin Runoff Model (LBRM) and data-driven Artificial Intelligence (AI) approaches, including Long Short-Term Memory (LSTM) networks and