12 software-defined-network Postdoctoral positions at Utrecht University in Ireland-University-Ranking-2024
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pathways and systemic change across environmental, economic and social dimensions; apply approaches like network analysis and complementary systems-change-oriented methods to national-scale case studies
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Mediterranean NBS Community of Practice as a Social Innovation Network”. Building on insights from earlier translocal social innovation research (TRANSIT, URBANA, ARTS), the postdoc will kickstart and facilitate
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-source geospatial data (e.g., network, satellite); experience with GIS and GeoAI-related model development and evaluation; an interest in GIScience, health inequality and health system resilience; strong
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you the opportunity to further develop your academic skills and contribute to groundbreaking research. Next to that it provides a unique opportunity to network with an extensive scientific and cultural
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of NBS. This position centres on “Task 3.3 – Establish a Mediterranean NBS Community of Practice as a Social Innovation Network”. Building on insights from earlier translocal social innovation research
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: select and co-develop indicators and methods to assessing circular transition pathways and systemic change across environmental, economic and social dimensions; apply approaches like network analysis and
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postdoctoral researcher in the NWO KIC project STURDI-Water (Storage, Upgrade, Reuse and Distribution of Water in regional collaborative networks), you lead research aimed at identifying the key success factors
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or functional information and contextual metadata such as crop genotype, soil and environmental conditions; define and evaluate self-supervised learning objectives and embedding strategies, and benchmark
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or neural networks) on CFD data to develop a fast, data-driven wind field predictor. You will combine this surrogate model with AeoLiS and evaluate the accuracy of the new model setup by applying it to
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information and contextual metadata such as crop genotype, soil and environmental conditions; • define and evaluate self-supervised learning objectives and embedding strategies, and benchmark their added value