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thymus and to chart the role of the thymic microenvironment in this process. This will be achieved through the use of state-of-the-art technologies, such as multimodal single cell approaches (CITE-seq
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professional domains and a desire to ask big-picture questions. Preferred Qualifications: Familiarity with natural language processing (NLP) or network science. Experience with machine learning libraries like
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in medical image processing, in particular MRI - Knowledge of MR spectroscopy (or a strong desire to learn it quickly). - Expertise of artificial intelligence techniques and deep learning - Proficiency
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-throughput Application and further development of X-ray methods (scattering, spectroscopy, imaging) using both in-house infrastructure and synchrotron beamlines Execution of operando studies to unravel
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. We advance the understanding of dynamic processes to address global challenges, from mitigating the impacts of natural hazards and sustaining our habitat amid global change to responsibly managing
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implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning
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these questions, we use a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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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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preferably start around 01-12-2025 and requires knowledge of geometric image processing and machine learning. Information As Post-doctoral Researcher within VICI Project "Geometric Learning for Image Analysis
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and practical skills in the field of data science, especially multimodal data analysis. Experience on image processing via machine learning. Programming skills (e.g., Python) are required. Ability