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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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-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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at the Geophysical Institute , University of Bergen. The position is offered for a fixed-term period of 3 years, with the possibility of a fourth year dedicated to career-development activities, such as teaching
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of data-driven approaches in computational mechanics. The research activities related to this position pursue the development of data-driven computational mechanics that will be subsequently applied
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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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in the process of societal change. Our common direction is driven by consideration for green and sustainable change and equitable social development, through new ways of managing natural resources and
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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