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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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imaging technologies, such as cryo-electron tomography (cryo-ET) and expansion microscopy, have been pivotal in advancing our understanding of the structure and function of motile cilia. In contrast
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computational research. In particular: • A high-quality imaging platform • A dedicated biocomputing hub that guarantees reliable data storage, management, and advanced analytical capacity. Our laboratory is
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“Female Heart”. The researcher position has a temporary financing for two (2) years from 01.11.25. About the project/work tasks: Advanced image analysis of ultrasound images Statistical analysis
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sustainable, there is an urgent need for innovative solutions to reduce carbon emissions during synthesis, enhance the durability of materials and improve recycling processes. Be part of this change and explore
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to function, and this process is tightly regulated by a network of chaperone proteins. At the heart of this network is Hsp90, a molecular chaperone essential for the folding and maturation of at least 20% of
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knowledge or hands-on experience with Python, ImageJ, and/or MATLAB is highly beneficial for image processing and data analysis. Communication: Excellent written and spoken communication skills in English are
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for realizing the physical learning paradigm developed at AMOLF. You will integrate these memristive devices with the reconfigurable nonlinear processing units (RNPUs) developed in the NE partner group to harness
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PhD Scholarships in Piezophotoacoustic Technology for Minimally Invasive Endoscopy - DTU Health Tech
right research project for you. The PhD research project aims to explore and develop probes for ex-vivo and in-vivo applications for tissue imaging, classification and identification by exploiting
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laboratory (wind-wave facility) experiments, using state-of-the-art imaging techniques developing computational codes to process and understand large experimental datasets (e. g., image processing) analyzing