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benchmark chemometric and physics-informed machine learning models to monitor, forecast, and ultimately control critical process parameters, implanting these models in advanced control frameworks to optimize
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engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 2 Oct 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Is the job
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15th October 2026 Languages English English English The Department of Energy and Process Engineering has a vacancy for a PhD Candidate in Integrated Energy Systems for Sustainable Digital
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
(ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning
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characterization hardware (e.g., source meters, potentiostats, impedance analyzers) and basic signal processing, while interest in machine-learning-based process control is a plus knowledge of fibre-reinforced
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selection criteria Knowledge/experience with control engineering, information fusion and/or data assimilation, marine technology Knowledge of and hands-on experience with machine learning and/or statistical
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candidate for a full-time (100%) PhD position for 3 years. You will join the research group Power Electronics and Electrical Machines (PEM) at IEL, where we foster an open, inclusive, and collaborative
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. We are seeking a candidate motivated to explore the how emerging technologies – such as machine learning, generative AI, and extended reality (XR) – impact societal preparedness planning required
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or more of: GIS and spatial analysis, environmental sensing or the Internet of Things, data analysis, AI or machine learning, and simulation or environmental-performance modelling within a urban context
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? To address these questions, the PhD candidate will develop and evaluate new decision-support methods that combine optimization and machine learning. Particular attention will be given to methods that can