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modelling, or modelling of physical/dynamical systems. familiarity with AI/machine learning/system identification techniques and their application to engineering problems. knowledge of digital twin concepts
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. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern machine learning methods as well as in biological data analysis are needed for the position
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simulation and real-world robotic systems. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a
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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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delegated by the Principal Investigator Job Requirements PhD degree in Electronic Engineering, Computer Science, or related field Knowledge of pinching antennas, wireless communications, and machine learning
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systems using computer vision, quantitative image analysis, deep learning methods for detection, diagnosis, and quantitative analysis of abnormalities with multimodal data, including clinical and
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qualification; (ii) demonstrate research skills in imaging science and machine learning, particularly on image reverse engineering, fake image and video detection, statistical detection models and mathematical
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protein foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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7th May 2026 Languages English Norsk Bokmål English English PhD Fellowship in Quantum-Safe Blockchain Technologies Apply for this job See advertisement Job description The University of Stavanger
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: Preferably PhD degree in Computer Science or equivalent. Have a solid foundation in the following areas: Computer Vision, 3D vision; Strong publication records in top-tier computer vision and machine learning