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for the construction industry and enable CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve
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transient electromagnetic (TEM) data. A key task will be to conduct numerical sensitivity analyses for potential acquisition protocols employing both FEM and TEM data, with an eye towards optimizing field
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transient electromagnetic (TEM) data. A key task will be to conduct numerical sensitivity analyses for potential acquisition protocols employing both FEM and TEM data, with an eye towards optimizing field
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time
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water quality. You will be embedded within the Ethio-Nature project, funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use
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-based simulation for process optimization. Developing advanced numerical models for the coupled heat and mass transfer in the float zone process including 3D computational fluid dynamics (CFD) models