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fluxes and acidification in global marine P cycling through a combination of field work in the North Atlantic Ocean, laboratory experiments and numerical modelling. A scientific cruise with our research
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uncertainty in downstream predictions; • fine-tune ARCA for tasks including microbial root competence and crop-relevant outcomes, and iteratively improve the model using experimental Design-Build-Test-Learn
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and uncertainty in downstream predictions; fine-tune ARCA for tasks including microbial root competence and crop-relevant outcomes, and iteratively improve the model using experimental Design-Build-Test
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field. Demonstrated experience with numerical groundwater modelling, preferably including variable-density flow, solute transport, freshwater-saltwater processes, aquifer storage and recovery
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laboratory experiments to build an eMRV framework supplementing traditional MRV. Observational approaches include deployment of passive chemical samplers, quantifying organic matter export, examining eDNA
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journals. Your qualities You have a strong background in data science and experience applying machine learning and other AI techniques. You are interested in applying these techniques to physical systems, in
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experience working in facilitating deliberative and participatory processes in social- ecological systems; Affinity with the water management sector; Demonstrable skills in qualitative research and scientific
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partnered pilot sites (in the Baltic, Mediterranean, and Atlantic) complemented with laboratory experiments to build an eMRV framework supplementing traditional MRV. Observational approaches include
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metanetwork for sharing knowledge and experience on NBS across the Mediterranean. Concretely, the postdoc will: Design and facilitate translocal community interactions, including social innovator dialogues and
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and