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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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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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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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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
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lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data
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like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available