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to establish DUV-based nanosizing as a viable, adaptable methodology. To achieve this, we will develop low-cost hyperspectral DUV imaging approaches combining holographic imaging with Fourier transform methods
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inclusion of patients, communication with the clinical providers, acquisition of images amongst others. In parallel to the clinical study, focus will be on improving PET/MRI acquisition and developing
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, pre-operative determination via endoscopies and imaging remains unreliable for patient selection. The objective of this study is to develop an image-based machine learning prediction model to assess
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environment. Visible homelessness, such as encampments, is frequently seen as detrimental to cities’ public image. In response, public authorities are increasingly introducing laws and regulations
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seen as detrimental to cities’ public image. In response, public authorities are increasingly introducing laws and regulations that criminalise homelessness, prohibiting behaviours that homeless
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image and speech recognition, recommendation systems, and predictive analytics. Machine Learning / Generative AI Generative AI addresses the creation of multi-modal content in the form of text, images
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at the development of algorithms and models capable of learning from data, recognizing patterns, and signal analysis. Tasks include image and speech recognition, recommendation systems, and predictive analytics
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sources. Your research will focus on combining imaging data (e.g., cardiac MRI, echocardiography), signals (e.g. electrocardiography, intracardiac signals), clinical measurements, molecular and genomic
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of different expressive forms such as spoken or written language, (moving) images, animations, sound, gestures, or others. How do these expressive forms or semiotic modes interact to construct meaning? Which
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, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where applicable) how images interact with epigraphic frames and contexts of production and