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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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, multidimensional signal processing and audiovisual computing. We are a core member of IMEC, the world-leading research and innovation center in nanoelectronics and digital technologies. Our team is currently a
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technologies. The project employs an interdisciplinary approach based on collaboration among specialists in text and image analysis, natural language processing, large language models, vision-language models
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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prepared for changes to your work duties after employment. Required selection criteria You must have experience with imaging, image processing, and/or visualization, as well as excellent programming skills
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scale hydrological models The project offers the unique opportunity to connect novel processing and inversion techniques to experimental data from different regions and link the findings to relevant
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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: Experience with crystallization processes, soft matter physics, or porous media Familiarity with imaging techniques (optical/electron microscopy, X-ray tomography, or spectroscopy) Experience with cultural
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experience with imaging, image processing, and/or visualization, as well as excellent programming skills. You must have a relevant Master's degree in computer science, electrical engineering, imaging science
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning