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Technology, Department of Safety, Economics, and Planning. The PhD fellowship leads to a doctoral degree and gives you the opportunity to develop research expertise in an active and stimulating academic
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prediction of regional climate and extremes using hybrid physics-AI models. About the project/work tasks There is a growing need for subseasonal-to-seasonal (S2S; 2 weeks to 12 months) predictions of regional
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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engineering, semantic integration, and information modelling. The position will allow you to contribute to cutting-edge interdisciplinary research while gaining valuable experience relevant for an academic
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The University of Stavanger invites applicants for a PhD Fellowship in Data-Driven Optimization of Multi-Energy Systems at the Faculty of Science and Technology, Department of Energy and Petroleum
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artificial intelligence (AI) and an increasingly important force in a digital and data-driven world. Integreat develops theories, methods, models, and algorithms that combine data with general or domain
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synthesis of timed and probabilistic behavioral models for model checking, performance evaluation, and optimization. The overall objective is to establish formal foundations that bridge static engineering
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the effects of peatland restoration on hydrology and catchment biogeochemistry by means of a diverse set of methods, including field investigations, modelling and data-driven analyses. Your immediate leader
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an interest in investigating the effects of peatland restoration on hydrology and catchment biogeochemistry by means of a diverse set of methods, including field investigations, modelling and data-driven