34 computational-physics-"https:"-"https:"-"https:"-"https:"-"IFM" Postdoctoral positions at Aarhus University
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
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, physics, computer science, applied mathematics, or similar Required competences Strong background in image processing and analysis, especially Deformable image registration and 3D segmentation methods
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Experience in planning and conducting field-work Experience in planning and conducting laboratory work within nitrous oxide field measurements, soil sampling and the quantification of soil physical, biological
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, proteins and DNA origami constructs, and computational procedures for data analysis. The project is a collaboration between the single molecule biophysics and chemistry group at iNANO/Department
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computational scientists. The position offers a unique opportunity to work at the interface of landscape ecology, biodiversity science, climate mitigation, and sustainable agriculture, contributing directly to
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advice, and education. We offer professional laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international
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recruitment process is completed a final letter of rejection is sent to the deselected applicants. Letter of reference If you want a referee to upload a letter of reference on your behalf, please state
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that addresses these issues. The center brings together experts on climate impact research and process-based modelling of biogeochemistry, agronomy, biology and geography from Aarhus University and University
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laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department consists of nine
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will