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tracers. Specifically, you will use clinical molecular imaging data in combination with numerous methods (i.e., AI image analyses, PBPK modeling, immunohistochemistry, FACS). As a postdoctoral researcher
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experience in computational methods, which may include machine learning. This could involve medical image analyses and processing or biomarker quantification approaches. Experimental set up experience would be
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programming skills Preferred Qualifications Track record of top-tier machine learning publications Experience with machine learning on medical images Experience designing language model or vision–language model
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, computer vision specialists, and digital humanities engineers, both at the Paris Observatory and at other partner institutions. One of EIDA's core research areas concerns the plural definition of image
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project quantifying plant anatomical diversity, the developmental processes that generates this diversity, and how this anatomical diversity influences biomechanical properties. The ideal candidate will
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large animal models, lymphatic research, and MRI. The scholar will be supported by MR technologists (MRI studies), plastic surgeons (lymphatic surgery), radiologists (MRI studies and imaging evaluation
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software applications for data modelling, analysis, and visualisation. These applications will explore data acquired using imaging and spectroscopic techniques to provide information on the nature and light
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. The research group “Behavioral Neuroscience” focuses on decoding the exact mechanisms behind
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fibrosis. Together with the department of surgery, focusing on vascular surgery, we aim to to ulitse high-throughput biomaterial screening to identify the optimal physicochemical surface properties
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(BBB) dysfunction and edema in brain cancer, a major source of morbidity in the disease. Using radiomics, single-cell, and spatial omics, the project integrates imaging and molecular data to better