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The candidate will develop and benchmark zero-shot multimodal fusion models for rare disease prediction, using melanoma as a use case. The project integrates spatial and single-cell multi-omics data with
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advanced experimental and modelling methods to better understand the behaviour of industrial lubricants under extreme operating conditions. Lubricants in gears, bearings and other mechanical systems can be
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holographic information using natural and spatially incoherent light sources. However, the image quality of such systems is still limited by inaccurate models of incoherent light propagation and by the complex
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topography, meteorology, and historical fire events. Ultimately, you will use this model to simulate various land management scenarios and mitigation measures, translating complex spatial data
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neurodevelopmental huntingtin (HTT) modulation across multiple spatial and temporal scales using dedicated mouse models of controlled HTT expression, combined with advanced non‑invasive functional MRI and