134 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions in Norway
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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
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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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acquire this within two years of appointment. Competence in quantitative research methods, particularly longitudinal research designs and analysis, is considered an advantage. Much of the thesis advice
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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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. 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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prepared that specifies the competencies that the Research Fellow will acquire. Access to career guidance will be provided throughout the doctoral education. Research topic Ultrasound is a medical imaging
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with statistical or machine learning models, evaluation methodology, and messy real-world data, preferably within the biomedical medical domain A combination of academic and industry experience
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(SEM), multi-level modelling, Bayesian statistics, or approaches combining quantitative data analyses with machine learning. Documented experience in teaching and related activities, such as
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for Optimisation and Machine Learning (QONOMics) project sits at the heart of this expansion. The project focuses on the physics of networks consisting of coupled harmonic and Kerr-nonlinear oscillators. By studying
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embed user-defined conditions and structural constraints directly into machine learning routines to achieve high-resolution 3D interpolation of rock mass properties. These synthesized geotechnical fields