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Radboud University Medical Center (Radboudumc); 10 Oct ’25 published | Netherlands | about 2 months ago
Image Analysis Group (DIAG) at Radboudumc. We develop, validate and deploy novel medical image analysis methods, usually based on the newest advances in machine learning with a focus on computer-aided
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to functionally validate our proteomics discoveries with in vitro phenotypic assays and microscopy imaging. For this position, we seek a highly motivated biochemist or analytical chemist with proven expertise in
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aims to study how the brain processes pictures and sequences of pictures, like in comics. These PhD positions will study how sequences of images combine images together into hierarchic relationships
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affects the performance of plasmonic structures in photovoltaics and imaging. About this position In this position you will leverage materials with high thermal conductivities (e.g. 2D materials) to achieve
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to address clinically relevant biological question in cancer immunology. We aim to functionally validate our proteomics discoveries with in vitro phenotypic assays and microscopy imaging. For this position, we
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will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models for phenotype prediction, helping to accelerate the cell
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play a key role in the integration and analysis of omics data collected within the groundbreaking TREAT EARLIER trial. This trial is the first preventive study in the field of rheumatology globally and
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cell lines have been engineered and characterised, you will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models
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(GenIR), a new and rapidly evolving retrieval paradigm where generative models are used to directly generate document identifiers given a user query. This paradigm departs from traditional multi-stage
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generate document identifiers given a user query. This paradigm departs from traditional multi-stage retrieval pipelines and instead integrates the indexing and retrieval process into a single, end-to-end