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that has been generated in a prior laboratory-setting project. Specifically, we will integrate recent advances in artificial intelligence-based automated interpretation of medical images, and new knowledge
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you want to unravel the crystallization of phase change materials in heat storage devices? Do you like to work with advanced imaging techniques like CT and MRI? Do you want to understand the relation
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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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through a self-learning chip prototype, improving performance and durability in automotive applications. Specifically, this PhD project focuses on memristive materials as electronic realizations
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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than the actual DNA damage itself. In this project we will use innovative single molecule imaging procedures in combination with CRISPR-Cas9-mediated gene editing to for the first-time study the effect
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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for homeowners and potentially increasing awareness and willingness to take action. Combined with “connection workshops”, where households interpret thermal images with researchers and receive retrofit advice from
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of porous electrodes, advanced imaging and characterization, and validation in electrochemical cells. Marie Skłodowska-Curie Doctoral Networks are joint research and training projects funded by
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interpret thermal images with researchers and receive retrofit advice from professionals, the project aims to also improve the ability of households to take action. By addressing barriers to retrofitting and