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Field
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The Department of Agroecology at Aarhus University, Denmark, is offering a postdoctoral position in merging soil physics knowledge and data-driven modeling of soil processes and properties, starting
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components of Land-CRAFT. We have a team of researchers working at field, farm and landscape scales. The postdoctoral researcher specializing in landscape nitrogen-flux modelling will use state-of-the-art
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, metamaterials, and/or soil–structure interaction, and proven skills in experimental design and testing as well as numerical modelling (e.g., FEM). Experience with railway dynamics, soil mechanics, or nonlinear
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of biodiversity and model the potential future biodiversity recovery given during land use transformation and restoration in Denmark. This involves spatial and temporal optimisation and prioritisation of land for
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
excavation and ground response (e.g., geotechnical centrifuge testing, lab-scale TBM experiments). Probabilistic and reliability-based analysis applied to underground structures. Advanced subsurface
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initiatives include developing advanced aqueous emulsion and suspension systems for spray coating, predictive modeling of packaging performance, and optimizing packaging designs for high-value product
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understanding of WS/DP adsorption to solid–water interfaces, including soils, sediments, sludge, and model mineral/organic surfaces. Experimental studies will investigate polymer–surface interactions, adsorption
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Area of research: Scientific / postdoctoral posts Job description: GFZ is Germany's national centre for solid Earth research. We advance the understanding of dynamic processes to address global
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hub dedicated to advancing, implementing, and disseminating state-of-the-art applied economic problem-solving informed by data analytics solutions. The successful candidate will develop a strong applied
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models