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modalities in the rodent brain (3D light-sheet microscopy, spatial transcriptomics and MRI of the same brain) to learn MRI contrasts that can depict neuronal and glial cell density and morphology. You will
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University of Aberdeen - School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences | Aberdeen, Scotland | United Kingdom | 20 days ago
platform for delivering the required data for analyses. These include immunity, inflammation and thrombo-inflammation with data from multi-omic profiling of circulating immune cells, spatial transcriptomics
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modalities in the rodent brain (3D light-sheet microscopy, spatial transcriptomics and MRI of the same brain) to learn MRI contrasts that can depict neuronal and glial cell density and morphology. You will
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and organoid platforms, genome engineering technologies, advanced microscopy, spectral flow cytometry, single-cell and spatial genomics, proteomics and biomarker laboratories, and high-performance
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including sc/snRNA and spatial transcriptomics, patient-derived models, systems biology and AI platforms Opportunity to obtain a PhD within the U Strasbourg MD-PhD program Tenure track program at Inserm and U
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handling high-dimensional, single cell or spatial data. Interest in interdisciplinary work combining AI and cancer biology. Proven track record of scientific publication. Demonstrated experience in
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: https://www.ntu.edu.sg/ase We are seeking a highly motivated Research Fellow to join a research group led by Asst Prof Zeng Yiwen. The successful candidate will work on a multidisciplinary biodiversity
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focused on using spatial profiling and machine learning of human specimens in combination with functional experiments in animal models to understand cancer initiation, progression, and metastasis. We
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on understanding the earliest stages of high-grade serous ovarian cancer (HGSOC) by integrating spatial multi-omic profiling with computational analysis to define how precancerous lesions evolve into invasive
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, physical AI, and spatial AI. The RF will contribute to the development of innovative algorithms, data preparation pipelines, and experimental evaluations that are central to the Physical Vision Group’s long