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endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle tracking, and integrating these models into automated analysis and
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, with a particular focus on identifying and characterizing rare endosomal escape events. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle
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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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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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. The tasks include developing, training, and validating deep learning–based models for event detection and vesicle tracking, and integrating these models into automated analysis and imaging workflows. The work
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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division, Department of Information Technology, and is carried out together with researchers who develop methods in deep learning and image analysis with applications in life sciences and medicine
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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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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