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Chair of Biological Imaging 27.02.2026, Academic staff We are now seeking a highly qualified and motivated Ph.D. student (f/m/x) to drive the development of novel optical technologies addressing
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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
state-of-the-art imaging techniques with software-supported analysis of flow dynamics and mathematical modeling for the precise characterization of hemodynamic mechanisms in atherosclerosis. In research
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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 of Munich (TUM) are an integrated
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science/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
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other equipment at the Cellular Imaging Facility Operation and quality testing of fluorescence microscopes Training and support of users of electron microscopes Laboratory management Your profile
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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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automation integration and deployment of image segmentation algorithms for the automated assessment of thin-film solar cell fabrication processes (e.g. layer quality, defects, morphology) within NOMAD Oasis
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Course location Hamburg Description/content The Cluster of Excellence "CUI: Advanced Imaging of Matter", which is funded by the German federal and state governments, combines projects in physics
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Max Planck Institute for Biological Intelligence (Seewiesen site), Seewiesen | Germany | 11 days ago
Matlab or experience with Python and willingness to learn Matlab are required. Experience with functional data (fMRI, calcium imaging) or image processing is a plus. Experience working with birds is not
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time