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relies on combining polarized quasielastic neutron scattering (pQENS), inelastic neutron scattering (INS), and molecular dynamics with machine-learned potentials to gain insights into ion conduction
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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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fields both locally (field by field) and regionally (groups of fields). Data driven analyses will be complemented by physical reservoir modelling, with machine learning approaches to extract correlations
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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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previous experience in application of machine learning techniques within energy systems strong analytical skills and ability to collect, analyze and study the impact of low carbon technologies good knowledge
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26 Apr 2024 Job Information Organisation/Company University of Stavanger Department Department of Electrical Engineering and Computer Science Research Field Computer science » Computer systems
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Intelligence (AI) techniques such as Machine Learning can be applied for power distribution network analysis to create improved observability and implement proactive operational measures such as activation
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machine learning approaches to extract correlations from data first, and causal analysis after to extract causation. The position is funded by the NCS2030 centre. Qualification requirements You must have a