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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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imaging environment, investigating the transport and adsorption of emerging contaminants under groundwater-relevant conditions. Responsibilities As a key member of our team, you will contribute to a Novo
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. • Quantitative analysis of aquaporin trafficking dynamics using advanced fluorescence microscopy and 3D image-based vesicle segmentation. • Molecular engineering of aquaporin constructs to dissect trafficking
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segmentation of organs from medical images; generation of simulation-ready surface and volumetric meshes from segmentations; and modelling realistic boundary conditions, particularly the interaction between
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magnetic microscopes for contactless imaging of the magnetic field induced in biological tissue samples. Our aim is to build the world’s fastest, most sensitive microscope capable of resolving magnetic
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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on electrolysis (SDU) as well as Danish manufacturers of both heat exchangers (Danfoss) and thermoelectric generators (TEGnology). The goal of the project is to realize an actual prototype heat exchanger that can
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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and maintenance of monitoring buoys and related sensor systems. Apply image analysis and machine learning techniques to ecological datasets. Develop and implement multi-platform monitoring frameworks
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combining CRISPR-based endogenous protein tagging, advanced quantitative imaging, and biochemical approaches, this project seeks to uncover novel principles of replisome plasticity. A key long-term objective