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Field
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Benferhat (CRIL) and Carole Delenne (IUSTI). Urban water networks are critical infrastructures whose management relies on a wide variety of data sources: Geographic Information Systems (GIS), Closed-Circuit
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narratives through participatory research methods and stakeholder engagement. Apply spatial modelling tools (e.g., GIS) alongside AI-based techniques to generate risk and vulnerability maps. Analyze and
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succession. These datasets will be integrated within a GIS framework alongside existing offshore infrastructure, including production platforms, pipelines, subsea cables and offshore wind developments
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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-centred design in vulnerable neighbourhoods. By combining qualitative and GIS-based mapping, community-oriented visual design engagement methods, and stakeholder analysis at multiple scales and in diverse
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/or academic experience in organising events with farmers, as evidenced in the CV and/or motivation letter; Experience in Geographic Information Systems (GIS), as evidenced in the CV and/or motivation
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adaptation and human-centred design in vulnerable neighbourhoods. By combining qualitative and GIS-based mapping, community-oriented visual design engagement methods, and stakeholder analysis at multiple
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, MFA), or spatially-explicit environmental footprinting (incl. GIS) - Knowledge in the mineral raw materials sector and criticality assessment methods - Knowledge/use of LCA software (e.g. SimaPro
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, either with R or GIS programmes or equivalent Excellent writing skills Excellent written and spoken communication skills in English The ability to work independently while contributing to a collaborative
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and analyzing ecological or environmental data Experience with data handling and analysis in R Experience in working with spatial datasets, either with R or GIS programmes or equivalent Excellent