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in secure environments Background in medical image analysis (CT/MRI preferred) Knowledge of detection, segmentation, and model development methods Interest in multimodal or longitudinal clinical data
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assays, immune‑cell trafficking, and spatially resolved tumor–immune crosstalk. Lead image and data analysis, including programming in Python and experience with ImageJ/Fiji, to process microscopy data and
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by The Kempe Foundations. Project description Machine learning and artificial intelligence have had a major impact on medical image analysis in recent years. While CT and MRI provide highly
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of food ingredients. The research is focused on structural and physical aspects of food ingredients and food matrices using high resolution microscopy and image analysis. We are seeking a highly motivated
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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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imaging and image analysis, biochemical, as well as molecular biology approaches. Performs perfusion, cryosectioning, immunocytochemistry approaches, stereological counting and Matlabbased automated image
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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, a novel spatial discovery proteomics concept that integrates microscopic cell phenotyping with deep-learning based image analysis and global MS-based proteomics. This unique method was recently
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Paris to develop an optimised data processing pipeline for detecting and measuring very faint surface brightness in Euclid mission images. The following four areas will be explored: the properties
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A Postdoctoral Research Associate position in advanced MRI acquisition, analysis, and modeling is available in the Department of Radiology at the University of Virginia School of Medicine, under