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Field
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interest in the human brain. Programming experience (Python, MATLAB) and proficiency in spoken and written English is required. Experience with or an interest in microscopy, quantitative image analysis
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grounded image data. The research explores how AI-driven analysis can move beyond manual reverse-engineering workflows by automating feature extraction and structural interpretation while remaining robust
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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fascinated by science and its rapid progress? Are you intrigued by ever-improving microscopes and the images they provide? Are you passionate about interdisciplinary research? Do you want to contribute your
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spectrometry; combine imaging datasets with multi-omics, digital pathology and AI-assisted image analysis to generate integrated biological insights; analyze the spatial distribution of ADCs, antibodies and
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using optical tweezers and confocal fluorescence imaging, building on previous work (O’Brien et al., Nat Comm 2024). Together with our collaborators, you will produce materials for these experiments and
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will design, execute, and analyze single-molecule experiments using optical tweezers and confocal fluorescence imaging, building on previous work (O’Brien et al., Nat Comm 2024). Together with our
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instability remains poorly understood. This project will use advanced human cell models, molecular and imaging approaches to investigate how extracellular vesicles and other signalling mechanisms drive
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quality and hinder quantitative image analysis, even when serial sectioning approaches are employed. To address this limitation, an initial strategy will consist of homogenizing the refractive index
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. fluorescence microscopy, flow cytometry, image analysis or CRISPR/Cas9). Experience with mitochondrial biology, tumour–stromal interactions, extracellular vesicles, intercellular communication or therapy