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to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
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, water quality, and data science; collects and harmonizes data from a variety of sources; develops analyses and reproducible workflows for hydrologic and biogeochemical data; interprets results and
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: Using biogeochemical evolutionary models to simulate lifeless and inhabited worlds, and Developing disequilibrium-, redox-, and information-based metrics to understand and quantify the influence of life
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well as a strong interest in climate science and biogeochemical cycling. Previous experience applying Bayesian inference, data assimilation, inverse modeling, and/or probabilistic machine learning
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measurements, behavior, and modeling; its connection with the biogeochemical cycling of C, N, and P, and fundamental biological and chemical processes controlling organic carbon sequestration in the soil
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to biogeochemical cycing of essential elements in the soil system. We also welcome potential candidates who are eager to develop their own research projects within these areas. We value collaborative and
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environmental conditions and the analysis and modelling of the consequences of interventions in biogeochemical cycles. The social science methodology is included in the context of human ecology, political ecology
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focused on the physical and biogeochemical modeling of the ocean using primarily the Regional Ocean Modeling System (ROMS). The OMG is a recognized leader in regional ocean data assimilation and ocean
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Sensing group, https://www.marsens-ugent.be/ ) investigates the vital link between marine ecosystems and ocean carbon cycling using innovative remote sensing and in situ observation technologies. Our