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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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algorithm-driven world. Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning
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to quantify the evolution of ocean-continent subduction orogens and comparing first order model results with mountain belts on Earth. Investigating how the combination of tectonics and climate-driven surface
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combining field sampling with laboratory experiments and statistical modelling to quantify genetic variation in complex traits, the candidate will test whether these contrasting histories have driven
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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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28th September 2026 Languages English English Norsk Nynorsk English PhD Research Fellow in Data-Driven Computational Mechanics for Offshore Wind Energy Apply for this job See advertisement UiB
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photovoltaic generation, hydrogen production, and storage alternatives with microgrid-driven power distribution. Through advanced modelling and optimization techniques, this research aims to identify optimum
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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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of previously published information geological restorations and basin modelling to improve the understanding of the interplay between tectonic and sedimentary processes, particularly where salt is present