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Machine Learning Group, Department of Engineering, CambridgeMLG Cambridge About Us News Research Publications People PhD Admissions Blog Latest News Papers with MLG authors to appear at ICML and
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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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of Visual Intelligence is to develop novel, innovative solutions based on deep learning to extract knowledge from complex image data. Deep learning, aided by machine learning techniques in general, has led
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integrated circuits (PIC). An optical set-up will be used to characterize the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis
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resulting precipitation and extreme weather. We study global and regional climate change and are at the core of international community climate modeling efforts that also involve AI and Machine Learning. We
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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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(Python, Matlab, etc.) as well as in modeling polymerization processes or using machine learning techniques is highly desirable. Good command of written and spoken English is a must (if preselected, an