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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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. 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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operationally safe position in real-time. This research focuses on real-time multi-objective optimization of wells, that may be achieved with a mixture of algorithmic and machine-learning approaches. Updating
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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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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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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
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Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 4 Jan 2026 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is the
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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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have experience with applications of machine learning and deep learning on medical image data that you have experience applying methods within generative artificial intelligence to medical images and
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. The PhD Fellow will be affiliated with the Computer Networks Research Group (ComNet) at the department. The proposed PhD project aims to advance the field of 6G vehicular networks by harnessing the AI