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Field
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diffraction tomography (ODT). The ODT will be used for imaging organoids and a correlative fluorescence microscopy must be integrated with the ODT. A close cooperation with COMBAT partners associated with WP 2
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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Roentgen’s Nobel Prize-winning discovery of X-rays enabled us to non-destructively image inside the body, birthing medical diagnostic imaging and revolutionising materials characterisation
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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garments into recycle, reuse, and manual-review streams; this PhD project tackles the core challenge of designing and optimizing a high-throughput hyperspectral imaging system, fused with complementary
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organoids, single cell multiomics, live cell imaging, and animal models. Tasks: to actively pursue the PhD project with the aim of characterizing the molecular and functional mechanisms of epithelial
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after damage. The applied techniques will include e.g. mouse and human kidney organoids, single cell analyses, proteomics, metabolomics, live cell imaging, and animal models. Tasks: to actively pursue
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epithelial cells and neurons in the intestine. The applied techniques will include molecular and cellular biology methods (e.g. neuroepithelial co-cultures, organoid cultures), advanced imaging (e.g
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heterogeneity; namely non-iid and domain shift, e.g. multi-modal data acquired by different scanners and imaging protocols. Publish and present scientific results at international conferences and high-impact
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors