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technologies and machine learning; and interact with collaborators and team members Prepare data reports for weekly meeting with PI and bi-monthly reports for weekly lab meeting Attend educational activities
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of living together, in this position with reference to migration-related diversity. The position contributes to advancing methodological innovation through the creative and reliable use of machine learning
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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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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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where AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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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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where AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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/guidelines . Minimum Number of References Required Maximum Number of References Allowed Keywords statistics, biostatistics, computer science, economics, health care policy, causal inference, machine learning
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, analysis and evaluation, and writing reports which contain descriptive, analytical and evaluative content. The purpose of this role is to acquire the professional skills needed to pursue a career path of his
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: • Graph-based learning and community detection: Identify cohesive and antagonistic groups within signed networks. • Machine learning and network embeddings: Measure consensus, polarity, and opinion shifts