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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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. 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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University of Stavanger invites applicants for a PhD Fellowship in in molecular modelling and machine learning for improved subsurface utilization, at the Faculty of Science and Technology, Department
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integration and optimized operation using machine learning and AI techniques as key drivers for improving system performance. The hired candidate will have the opportunity to work with cutting-edge energy
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Additional Information Work Location(s) Number of offers available1Company/InstituteDepartment of Electrical Engineering and Computer ScienceCountryNorwayGeofield Contact City STAVANGER Website http
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Work Location(s) Number of offers available1Company/InstituteDepartment of Electrical Engineering and Computer ScienceCountryNorwayGeofield Contact City STAVANGER Website http://www.uis.no Street 4036
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. The following is considered important in the assessment: that you have experience with applications of machine learning and deep learning on medical image data that you have experience applying methods within