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learning, medical image computing, biomedical engineering, medical physics, or related field Strong Python and PyTorch experience Solid publication record and ability to communicate research results
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Max Planck Institute for Medical Research, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 1 month 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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the excellence cluster Your profile PhD in physics or in a related discipline experience in phase contrast or coherent X-ray imaging advanced programming skills in a high-level computer language (Python or related
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well as an active engagement in the 3RTG activities. Requirements: successfully completed university degree (Master's, Diploma or equivalent) and relevant PhD in Computer Science, Computer Engineering, or related
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The Leibniz-Institut für Kristallzüchtung (IKZ) is a leading research institution in the field of science & technology as well as service & transfer of crystalline materials. Our goal is to enable
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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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for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field strong background in image processing and analysis, including deep learning
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of motion analysis in collaboration processing of images in preparation for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field
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the scope of BlueMat, taking advantage of the collaborative network within the excellence cluster Your profile PhD in physics or in a related discipline experience in phase contrast or coherent X-ray imaging
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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