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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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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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++ 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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: 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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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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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
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have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and spoken proficiency in (scientific) English is
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13C incubation approaches the current project will quantify dark carbon fixation rates in deep ocean settings and explore how environmental parameters affect the process rates. These approaches
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multiphysics - Strong interest in experimental techniques and analysis of experimental data - Skills in programming and signal and image processing - Taste for interdisciplinary environment: physics, chemistry