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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
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will apply cutting-edge imaging technologies such as volumetric EM, cryo-ET and state-of-the-art image segmentation to study changes in the cellular architecture of diverse fungal pathogens during
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imaging using deep learning. You will study the water imbibition in hierarchically porous Si‑based material systems across multiple length and time scales. These systems can manipulate fluid transport
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laboratory, phenotyping, and imaging infrastructure • Opportunities for international collaboration and participation in conferences and project workshops Application procedure Please submit your application
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/processes; formalise requirements; prototype traceability. Expected outputs include requirement templates and an initial workshop paper. Year 2: Integrate PlaTFoRm tools; evaluate coverage and fault
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unified artificial intelligence (AI) model capable of segmenting 3D medical images from standard clinical scans and generating 3D meshes across multiple imaging modalities. The project will also investigate
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on electrophysiological approaches (MEEG, iEEG) and signal processing, while in Maastricht, the partner team provides ultra-high-field imaging (7T and 9.4T fMRI) and AI-based modeling. The PhD student will be enrolled
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The molecular biosciences are undergoing a major paradigm shift – away from analysing individual genes and proteins to studying large molecular machines and cellular pathways, with the ultimate goal
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric
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. This studentship addresses this critical gap by leveraging recent advancements in plankton imaging data classifiers’ translatability across multiple instruments’ output. It will apply existing biodiversity policy