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Field
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Responsibilities: To independently undertake research on data-efficient object detection with key techniques in few-shot learning, transfer learning, image synthesis, etc. To produce research reports and/or
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 3 days ago
the recording of dendritic electrical signals using electrophysiological and imaging techniques. The fellow will be involved in setting the protocol for imaging voltage signals in dendrites of pyramidal
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performance. The project will leverage one-of-a-kind in-process imaging data to gain unparalleled insights into the laminate microstructure. The outcome of this work will be a predictive capability to quantify
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welcome to apply. Familiarity with image data analysis tools such as SPM, FSL, AFNI, and/or DSIStudio etc will be an advantage. Signal processing and programming skills (e.g. with Matlab, Python) will be
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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, and single-cell sequencing. Responsibilities Perform intravital surgery to implant optical imaging windows over draining lymph nodes for long-term, real-time tracking of T-B cell interactions. Develop
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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processes that use data-driven machine learning. Given the span of the IN-CYPHER programme, we are seeking multiple motivated research fellows. Unique in its scope, we are developing technologies that span
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-disciplinary team of researchers, including bioinformaticians, pathologists, oncologists, and computer scientists, and conduct original research on computational pathology. Digital pathology images contain rich