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(HGG) at the Department of Geoscience, we’re looking for a motivated postdoc for a 1-year postdoc position (with a possible extension for 1 more year) to advance groundwater modelling approaches as part
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, optical) to produce soil moisture maps. Designing and implementing deep learning pipelines for super-resolution of remote sensing data. Applying explainable AI (e.g., Shapley values) to interpret model
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nature-based solutions that enhance local recharge and support the replenishment of shallow groundwater systems in dryland environments. The project combines shallow FEM with deeper ground-based TEM
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nature-based solutions that enhance local recharge and support the replenishment of shallow groundwater systems in dryland environments. The project combines shallow FEM with deeper ground-based TEM
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, geophysics, mathematical modelling, or related fields. The ideal candidate should have: Experience with numerical climate models (such as EC-Earth or similar GCMs). Advanced programming skills in Python
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, geophysics, mathematical modelling, or related fields. The ideal candidate should have: Experience with numerical climate models (such as EC-Earth or similar GCMs). Advanced programming skills in Python
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-based carbon models based on unique datasets from long-term experiments and the national soil monitoring network for arable mineral soils. Your work will be based on methodological research for developing
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under wet and dry conditions at a field trial. Utilizing and validating digital sensors (soil probe, drone data) for crop water use determination. Crop modelling (e.g.) APSIM for perennial grain crops