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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | about 3 hours ago
machine learning models for detecting subtle, clinically relevant changes in arthritis imaging. Create robust algorithms for longitudinal disease monitoring and treatment-response prediction. Build a
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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assess how effectively imaging techniques capture or enhance the taxonomic resolution of traditional net sampling. Training You will gain skills in plankton taxonomy, image processing, ecological
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PhD Scholarship Develop multimodal machine learning models to predict glioblastoma treatment outcomes using imaging and clinical data. Work with real-world data from John Hunter Hospital in a
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, Human-Robot Interaction, Imaging Technology, Machine Learning, Medical Informatics, Medical Robotics, Natural Language Processing, Neuroinformatics, Optimization, Perceptual Inference, Probabilistic
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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80
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learning. Experience with Bioinformatics is a plus. Proficiency in quantitative analytical methods and computer programming (e.g., Python, C/C++) is essential. Experience with medical image analysis (e.g
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new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will benefit from the extensive and broad
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in multimodal imaging. Experience in machine learning is highly valued. You will support user-driven research projects and develop integrated data workflows spanning light microscopy (confocal, super
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, T., ... & Thouvenin, O. (2023). Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning. Journal of Medical Imaging, 10(3), 034504-034504. [3