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data to address priority questions in cancer care pathways, diagnostic delay, and treatment access. The role will involve advanced quantitative analyses, such as survival modelling, machine learning, and
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participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally leading
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not yet competitive for 5-year clinician scientist fellowships. This post is designed for applicants with a research interest in machine learning or data science approaches for patient stratification
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for project tasks and work independently without close supervision. • Someone comfortable asking questions and eager to learn. Position Details • Desired start date: January 2026 • Full-time: (40 hours
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-C05-i08 of the Recovery and Resilience Program, through the Foundation for Science and Technology - FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering
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, or related discipline, with an excellent academic record. Demonstrated expertise and leadership in computer vision and machine learning research, including object detection, multi-object tracking, and
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computer programming. Learning to approach novel analytical problems with effective and appropriate solutions. Preparing briefings, technical reports, and manuscripts for publication in professional journals
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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innovations in computer vision and computer graphics (segmentation, registration, tracking and visualisation) to enable real-time interaction for surgical planning and decision making. The project will provide
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert