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sciences, hydrology, or another suitable field. Strong expertise in environmental remote sensing and geospatial data analysis, and experience with at least one major observation technology or dataset type
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requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as
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community partners to ensure outputs reflect local priorities and inform adaptation planning. Duties may include: Develop spatially explicit computational models using machine learning, hydrologic, and energy
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, morphodynamic, and landscape evolution models. Field experience in geomorphological and hydrological surveys, including the use echosounders and Acoustic Equipment Use of personal computer and proficiency in
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, please refer to the link: https://research.nottingham.edu.cn/en/persons/alain-chong ; Prof. Ying Weng is Course Director for Computer Science with Artificial Intelligence, Professor in Computer Science
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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is part of the ERC-funded project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD
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collaborators. The position is part of the ERC Starting Grant “Actively learning experimental designs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate
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, please refer to the link: https://research.nottingham.edu.cn/en/persons/alain-chong ; Prof. Ying Weng is Course Director for Computer Science with Artificial Intelligence, Professor in Computer Science