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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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intelligence, computer science, remote sensing, geomatics, data science, or a forest/environmental science discipline with a strong quantitative or AI component Strong knowledge of machine learning and deep
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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25th September 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures PhD
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journal or conference on a relevant topic in machine learning, embedded systems, and edge intelligence Hands-on experience on Nvidia Jetson boards or other edge platforms Strong knowledge in deep learning
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of Computer Science, Norwegian University of Science and Technology (NTNU). The position offers the opportunity to work on cutting-edge research at the intersection of deep learning and computer systems. The successful
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learning, and statistics for bioprocesses). Personal suitability and motivation for the position. Qualifications considered an advantage Documented knowledge and experience in machine learning, deep learning
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
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for the position. Preferred selection criteria Experience or strong interest in one or more of the following areas is considered an advantage: Machine learning, deep learning, natural language processing or data
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of cryptographic implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning