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Field
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team of experienced researchers in imaging, machine learning, oncology, and pathology. We do not discriminate on the basis of sex, gender, belief, culture, place of birth or occupational impairment when
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learning, offers powerful solutions to automate these tasks and provide reliable real-time information. This doctoral project is part of a 5-year research chair on Computer vision applied to the swine sector
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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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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science, engineering, physics, mathematics or a similar domain. There is a strong preference for an applicant with a biomedical background. Experience with medical image processing, histopathology, computer vision
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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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engineering, electrical engineering, data science, or a related field. Skills in embedded systems development, electronics, or IoT (C/C++, Python, Arduino/ESP32, etc.) OR in machine learning and sensor data
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learning, or signal processing; familiarity with microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated