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Field
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Can AI learn to reason like a human - and recognise when it isn’t sure? This PhD tackles that challenge in a high-impact setting: interpreting 3D digital models of rock formations built from drone
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Experience with 2D or 3D biomedical imaging, quantitative or multimodal biological datasets. Familiarity with biomaterials, tissue engineering, scaffolds, hydrogels or 3D biological models. What are we looking
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/or scNMT-seq workflow for multimodal profiling of melanoma models and tissue samples. The project will translate the protocol developed by DC3 into 3D melanoma organoid and immune cell co-cultures
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impact. Furthermore, working at AMADE is not only a professional opportunity, but also being close to a stimulating environment with all our research lines in simulation and numerical modeling
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, generative AI, and blood flow modeling. You will: develop deep generative models (such as latent diffusion models, implicit neural representations, and flow matching) for uncertainty-aware 3D reconstruction
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models that includes breeding colony management and genotyping. Performs advanced 3D and spatial imaging (confocal, LSFM, Xenium spatial transcriptomics) and associated computational image analysis
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are still present. This PhD research aims to develop new and efficient AI-based solutions for processing LiDAR data (2D/raster and 3D/point clouds) and improve the detection of sub-canopy archaeological
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predict the behavior. You will be embedded in our team, which holds dedicated in-house expertise in 3D printing, pH-feedback systems and modeling, and you will have assistance on the various aspects
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to developing methods for iPSC culture, 3D cell models, and stem cell differentiation within the field of complex tissue regeneration. In this PhD project, you will work at the interface of stem cell biology
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Despite significant advances in numerical techniques and computing hardware, the high computational cost of large-scale 3D computational fluid dynamics (CFD) modelling remains a major challenge. A