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addressing significant biological and medical challenges. The Institute of Biological and Medical Imaging (IBMI) at Helmholtz Munich and the Chair of Biological Imaging (CBI) at the Technical University
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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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Package: Actively participate in a participant-driven co-design process to develop a framework for returning molecular and imaging data to study participants Contribute scientific content to patient
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projects within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or
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analysis. The Institute of Biological and Medical Imaging (IBMI) at Helmholtz Munich and the Chair of Biological Imaging (CBI) at the Technical University of Munich (TUM) are an integrated, multi
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. The Institute of Biological and Medical Imaging (IBMI) at Helmholtz Munich and the Chair of Biological Imaging (CBI) at the Technical University of Munich (TUM) are an integrated, multi-disciplinary research unit
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Max Planck Institute for Medical Research, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 2 months ago
Developing new fabrication processes for CaF₂, sapphire, and BK glass Collaborating closely with industry partners in areas including lasers, imaging cameras, and display technologies Preparing and supporting
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to biomedical data is a plus Experience in image processing techniques, such as segmentation, is of advantage Ability to quickly grasp new concepts and work independently on complex problems Ability to work as
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expertise, we are creating functional genetic maps using conditional CRISPR/Cas9-based single and higher-order knockout perturbations combined with single-cell expression profiling and imaging. We expect
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), proteomics (LC-MS/MS), (epi)genomic data processing, multi-omics integration, machine learning approaches for high-dimensional data, confocal / two-photon imaging, tissue clearing and light-sheet microscopy