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across HHMI. The Foundational Microscopy Image Analysis (MIA) project sits at the heart of AI@HHMI. Our ambition is big: to create one of the world’s most comprehensive, multimodal 3D/4D microscopy
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imaging, plant growth / physiology, molecular biology, 3D printing, electronics or programming experience would be looked on favourably. The ideal candidate would have a thirst for knowledge, enthusiasm
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approach, Timelapse-Patch-Seq (TiPS), combining electrophysiology, 3D imaging and transcriptomics at the level of individual synapses. Main tasks Design and conduct a postdoctoral research project on long
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quantitative image analysis, single-cell RNA-seq, as well as 2D cell line and 3D organoid culture (including maintaining stocks of lines). Additional techniques and experimental procedures may include spatial
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on existing methods, including a foundational model for mapping 2D mouse images to a canonical 3D mesh, with the goal of extending to full 3D reconstruction. This will enable inferring canonical 3D postures
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reconstruction algorithms, enabling high-resolution, high-speed, and deep 3D quantitative imaging. In collaboration with the Department of Clinical Medicine (UiT), the system will be evaluated for dynamic imaging
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neutrophils, tumor cells, CD8+ T-cells, and cancer-associated fibroblasts, within a biomimetic 3D hydrogel that replicates the tumor microenvironment. Observing neutrophil behaviour: Automated image analysis
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electrodes using various electron microscopy techniques (e.g. FIB-SEM); (ii) Reconstruction of the 3D structure through image analysis; (iii) Analysis of images from new and aged membrane-electrode assemblies
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genotyping), tissue sectioning and immunohistochemistry/immunofluorescence, confocal and light sheet imaging and quantitative image analysis, single-cell RNA-seq, as well as 2D cell line and 3D organoid
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Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning Applied domains: Machine Learning for Cybersecurity, AI for 3D Imaging, Recommender