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partners (Multiconsult, Argeo) aim at improving and refining present AI classification methodology using novel combinations of statistical methods and Deep Learning (DL). The position is for a period of four
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deep learning. The position is for a period of four years. The nominal length of the PhD programme is three years. The fourth year is distributed as 25 % each year and will consist of teaching and other
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Torbein Kvil Gamst 28th May 2024 Languages English English English Faculty of Science and Technology PhD Fellow in the intersection of information theory and deep learning Apply for this job See
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at the Department of Mathematics and Statistics , Faculty of Science and Technology and focuses on developing new methods for incorporating heterogeneous data into deep learning algorithms. The project is an
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for time series and graphs. One of the main goal will be to push the boundaries in the field of relational deep learning by: Creating innovative tools (novel architectures, training strategies, etc
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of relational deep learning by: Creating innovative tools (novel architectures, training strategies, etc,…) for processing spatio-temporal data, e.g., multiple time series whose relationships are described by a
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financed by the RCN (Research Council of Norway) funded project “RELAY: Relational Deep Learning for Energy Analytics ”. The main focus of the research will be to advance the state-of-the-art of machine
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: [email protected] Qualifications We are looking for a motivated candidate who is independent thinking and enjoys working in a team. The suitable candidate should have expertise in deep learning and a strong
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deep learning-based solutions to extract knowledge from complex image data to enable new innovations. Deep learning has led to a range of new image-based technologies that is rapidly changing society
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deep learning-based solutions to extract knowledge from complex image data to enable new innovations. Deep learning has led to a range of new image-based technologies that is rapidly changing society