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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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. About the position The successful candidate will do research on how to improve precision and accuracy in forest soil carbon stock estimates within the project “A better check on soil carbon - a novel
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, interviews, thematic analyses, researching and writing up case studies, data presentation, etc. Candidates also proficient in quantitative methods will be highly valued, but such skills are not essential
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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teaching in the field of flexibility, advanced power electronics, battery energy systems and microgrids. We are currently formed by 30 researchers from 15 different countries, which continuously provide us
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integration. Strong communication skills and a collaborative mindset. Knowledge, Skills, and Abilities Solid knowledge of power-system analytical methods (power flow, economic dispatch, state estimation
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studies, data presentation, etc. Candidates also proficient in quantitative methods will be highly valued, but such skills are not essential. The postholder will be a member of the Saïd Business School’s
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://netzeroamerica.princeton.edu/the-report) that developed uniquely-granular spatial, temporal, sectoral, technological, and socio-economic analyses to vividly describe at politically and societally relevant scales different
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vision, animal welfare, and behavior. The position is aimed at a method-oriented researcher interested in developing and applying computer vision–based methods in real-world settings. The project is part
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, geolocation and AI-enabled communication and networking systems. In these roles you will undertake research centered on the intersection of networking, communications, geospatial methods and machine learning