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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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develop automated pipelines for segmenting and analysing X-ray computed tomography (XCT) images of titanium layers. This includes deep learning-based image analysis and the extraction of quantitative
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algebra, signal processing, probability, random processes, statistical machine learning, deep learning. About the employment Salary: Individual salary setting. Starting date: 1 September 2026 or as agreed
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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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integration (up to 3 million cells) using deep learning-based approaches, hierarchical clustering, and cell type annotation benchmarked against published CRC atlases Deconvolution and TME characterization
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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students