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scientific skills for image analyses and opportunities for students to attend relevant workshops and conferences in the field of imaging, neuroscience, and aging. We also encourage students to upskill
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. This position is part of a strategic research collaboration between DTU and the Korea Advanced Institute of Science and Technology (KAIST), combining expertise in biomedical signal processing, neuroengineering
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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of an image analysis algorithm for particle tracking and speed quantification. Requirements for candidates: Essential: BSc and MSc in biochemistry, biology, biophysics, biotechnology, biomedical engineering
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). Lesion and anomaly detection in biomedical/industrial imaging. Development of robust models transferable to real-world environments. Use and development of 3D reconstruction techniques. Candidate Profile
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respond to different treatments. Working at the intersection of biomedical engineering, computational modeling, and clinical cardiology, you'll: Analyze comprehensive clinical data from our ongoing
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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well as with members of the Biomedical Mass Spectrometry and Systems Biology Section at the Department of Biochemistry and Molecular Biology. Lab and Research Environment You will be part of the research group
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about biomedical research or cell biology? Ready to make groundbreaking contributions to understanding ciliopathies through innovative machine learning approaches? Join the Cilia-AI consortium, and embark
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8.600 students, the Medical University of Vienna is one of the largest top-level biomedical research and teaching institutions in Europe. The Department of Medicine II is looking for a part-time (30 hours