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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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the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing factors. The LCSB recruits talented scientists from
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with other members of the lab, and attend mandatory lab meetings. Perform assigned lab duties to assist in the maintenance of stock reagents or the general operation of the lab, and various other duties
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: Job Requirements: Required: PhD in computer science, biomedical informatics, electrical engineering, or a related field Demonstrated expertise in machine learning, deep learning, and image-based
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neuropsychological assessment with brain imaging data (e.g., functional MRI, structural MRI, EEG) Proficiency with neuroimaging analysis software or electrophysiological data processing Experience conducting clinical
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on the effect of the language environment on auditory speech processing during early development. The postdoctoral researcher will actively participate in experimental design, data collection and analysis, and
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new experimental approaches and imaging strategies, and there is ample opportunity to initiate and coordinate experiments together with the PhD students while contributing to their supervision alongside
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-doctoral candidates willing to write research proposals on the functional morphology of organisms via dynamic X-ray imaging. Position X-ray imaging is a powerful method for analysing functional morphology
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bioinformatics. The role will be responsible for developing and characterising human dorsal root ganglia cultures to benchmark the newly developed iPSC derived organoid model systems. This will include processing
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Researcher (R2) Positions PhD Positions Application Deadline 2 Mar 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract Other Type of Contract Extra Information Linked to project grant Job Status Full