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Field
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strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have experience with explainable and/or
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural networks). You have experience with deep
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. • Karimi H. et al., “Wavelet Based Protection of Microgrids”, IEEE Transactions on Smart Grid, 2019. • Heidari A. et al., “Deep Learning for Fault Detection in Smart Grids”, Applied Energy, 2021. • Wen L. et
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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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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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contain well-informed methodological considerations. To find out more about the department please go to https://www.cbs.dk/si. ; A particular quality of the department is in the use of empirical
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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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doctoral programme in Technology within three months of starting in the position. (https://www.usn.no/english/research/postgraduate-studies-phd/our-phd-programmes/technology/). The doctoral programme
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% of your time), including tutorials and supervision of Bachelor’s theses. This is what we ask of you This is an interdisciplinary project that combines machine learning and AI, probabilistic risk