18 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" scholarships at University of Stavanger in Norway
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collaborations. We seek applicants with strong analytical skills, background in computational fluid dynamics and/or machine learning, and a genuine interest in advancing reliable scientific machine learning
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numerical models and machine learning tools to predict loads, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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the competencies that the Research Fellow will acquire. Access to career guidance will be provided throughout the doctoral education. Research topic We welcomes applications specialising in several fields
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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Fellow will acquire. Access to career guidance will be provided throughout the doctoral education. The University of Stavanger funds the position. It is connected to the international research project
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acquire. Access to career guidance will be provided throughout the doctoral education. Research topic The PhD Fellow will conduct research in industrial economics with a primary focus on the seafood
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prepared that specifies the competencies that the Research Fellow will acquire. Access to career guidance will be provided throughout the doctoral education. Research topic The PhD Fellow will join the
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resilience of bridges under climate change-induced hazards such as flooding, scour, and debris impacts. The research aims to develop advanced numerical models and machine learning tools to predict loads
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to: compositional multiphase reservoir simulation upscaling or screening methodologies optimization of well positions and control strategies economic assessments machine learning or proxy-model based methods field