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computer vision, deep learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities - including scanning electron microscopy (SEM), photon emission microscopy
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data, how it can be extracted, and how
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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engineering disciplines, including structural mechanics, hydrodynamics and machine learning Strong programming skills in Python and/or MATLAB Experience with scientific computing, CFD/FEM software, potential
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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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University of Science and Technology (NTNU) has a vacant position as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the
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learning and deep learning applied to electroencephalography in the context of brain-computer interfaces, including experience with MATLAB and Python and in the design and conduct of experimental studies
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monitoring. Candidates should have a background in computer science, AI, machine learning, affective computing, computational psychology or related areas. Strong programming skills are essential. Funding
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and machine-learning methods for multi-objective optimization of efficiency, reproducibility, and operational stability Study intrinsic material stability, light-induced phase segregation, ion migration