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Science. Explore new possibilities in the study of 2D and 3D magnetic microstructures using a mix of static and dynamic magnetometric methods, as well as advanced static and dynamic magneto-optical domain
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in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python and/or C/C
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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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, or related 3D stem cell models Proven skills in advanced imaging and quantitative analysis of dynamic processes Interest in interdisciplinary approaches at the interface of biology and physics Excellent
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positions are flexible in terms of research direction within 3D vision and graphics with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested in static and dynamic 3D
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such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D observations. Generating