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Cancer is a leading cause of death globally, and analyzing digital pathology images for cancer diagnosis and treatment is a complex problem due to the high data volume, variability, and computational
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, BCI), physiological data, and medical image/microscopy analysis. Excellence in foundational and applied research, demonstrated by publications in leading AI/ML and medical imaging venues (e.g., MICCAI
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of the identified structures via stereolithographic, 3D printing and textile techniques like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and
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will be responsible for data management and infrastructure, implementation and development of analysis pipelines. You will work on different datasets and help our users with image and downstream analysis
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. Your mission Understanding disease requires weaving together many layers of biological and clinical information. By fusing multimodal data including genomics, imaging, spatial omics, and patient records
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and its plasticity. The project will be carried out by applying “all optical physiology” methods, i.e. a combination of calcium imaging with optogenetics, in freely behaving animals in Paolo Medini’s
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methods such as NGS, chemical proteomics, and imaging. As the computational lead at CBGE, you will coordinate data-driven projects, spark collaboration across research units, and serve as the key bridge
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protein engineering, synthetic chemistry, nucleic acid technology, and state-of-the-art biophysical methods (spectroscopy, live cell imaging and nanotechnology) to address cutting-edge questions in biology
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, performing drug sensitivity analyses, and handling animals, including IV injections, tumor isolation, and imaging, when necessary. The general lab works include cell culture, reagent preparation, western
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on processing and analyzing large sets of medical brain imaging data. We have amassed large quantities of structural MRI (used to measure brain structure), diffusion MRI (used to measure brain connectivity) and