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-driven machine learning, is seeking to recruit a fulltime PhD student at the UiT The Arctic University of Norway for a cross-disciplinary project across machine learning, statistics and logic, which is
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Description The position A PhD position in machine learning for graphs and time series data is available at the Department of Mathematics and Statistics , Faculty of Science and Technology . The position is
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combination of geophysical, geological, and petrophysical data with advanced data processing techniques, including multi-component elastic full-waveform inversion, AVO inversion and machine learning, will be
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for feature extraction, which can serve as parameters in simulation. Alternatively, machine learning methods can be employed for comparison with the primary analysis-based approach. The objectives of this PhD
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the field of Computational Material Modeling, related to the “Designing Solid Electrolytes for Li- and Na-ion Batteries using Deep Learning-based Models (DeepSolo)” project. The PhD candidate will be part of
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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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solutions. The research will navigate the complexities of adversarial machine learning attacks and defenses, formulate robustness metrics, and emphasise the challenges of large language models (LLMs
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: knowledge and experience within marine technology, in particular, offshore wind turbine technology knowledge and experience within machine learning motivation and potential for research within the field
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solutions. The research will navigate the complexities of adversarial machine learning attacks and defenses, formulate robustness metrics, and emphasise the challenges of large language models (LLMs
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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