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to achieve them. Acquire new knowledge quickly and use existing knowledge in new ways. Work constructively under pressure or in the face of adversity. Demonstrate strong problem-solving abilities with a
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at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to conduct cutting-edge research on AI-based forecasting and analytics for shipbroking and maritime decision support
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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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. The supervision team includes: Prof. Ivan Depina – main supervisor and coordinator, probabilistic modelling, scientific machine learning Prof. Mohamed Hamdy – building performance simulation, building automation
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consortium-wide large datasets analyses Active participation in LALP Lab activities Required selection criteria You must have completed a doctoral degree in cognitive science, psychology or computer design
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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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methods at the intersection of scientific machine learning, reservoir/production engineering, and process systems engineering. This position will focus on the research of physics-informed AI-based models
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period of 3 years. The position is subject to external financing through the RCN funded project "Quantum Oscillator Networks for Optimisation and Machine Learning" (project number 358752). About the
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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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, and artificial intelligence methods. Assess drought stress and identify physiological traits associated with drought tolerance using advanced imaging technologies. Develop machine learning and deep