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Are you interested in developing new image analysis and machine learning methods for cancer diagnostics and clinical decision support? Would you like to work in a multidisciplinary team together
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collaboratively Meritorious qualifications: Experience with machine learning or deep learning Experience with computer vision or image analysis Experience with 3D data, geometric representations, or parametric
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collaborators in computational biology, machine learning, and imaging-based profiling. The position involves leading independent research projects at the interface of machine learning and biology, with a strong
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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. Experience in deep learning, computer vision, or neural network development. Experience with live-cell microscopy, fluorescence microscopy, or analysis of 3D/4D image data. Experience in cell biological
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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· Develop and apply transformer-based foundation models and machine learning methods for large-scale epigenetic datasets · Integrate longitudinal data and biological prior knowledge into AI models · Actively
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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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organismal fitness, using MOO techniques, machine learning and genome-wide association studies. Yeast and bacteria are your primary models, but the analytical framework you develop will be broadly applicable
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral