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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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candidate will facilitate code collaboration within the Modelling of Atmospheric Processes (MOD) department and, in particular, manage IAP code contributions to the upper atmosphere components of the ICON
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candidate will join an exciting research program exploring a newly emerging paradigm in immunology: mitochondrial transfer and synthetic organelle therapy. Using newly developed genetically engineered mouse
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. In association with the SFB 1436, Neural Resources of Cognition (supported by the German
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world with increasing digitalization and medical needs. Our research integrates materials chemistry, biological processes, physical analysis, process engineering and data science. We collaborate with
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Vision) for image data analysis. Data preparation, annotation, and training of models for structural recognition in biological and textile samples. Building automated pipelines for image analysis
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. In association with the SFB 1436, Neural Resources of Cognition (supported by the German
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, industrial and energy systems into a sustainable bio-based circular economy. We develop and integrate techniques, processes and management strategies, effectively converging technologies to intelligently
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well as the management of highly complex multimodal data. This role will focus on providing, developing, evaluating, and productizing computer vision methods for the analysis of video and imaging data that are generated
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: Mathematical modelling will be used to investigate the processes and consequences of disease progression from liver fibrosis to cirrhosis to hepatocellular carcinoma (primary liver cancer), to select specific