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technologies have exploded in recent years, generating multi-dimensional data from millions of cells and thousands of molecular features. Understanding these data and harnessing their insights remains an ongoing
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of the post-doc is to study how innovations in AI, especially adaptation of Large Language Models (LLMs) architectures for time-series data, can be used in study of aging, health span, and longevity
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experimental and computational approaches are employed to shine light into key biological processes during the life of parasitic flatworms. Large-scale sequencing datasets (‘omics’) are generated and analyzed
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and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical
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of large strain (antigenic) diversity, network descriptions of strain structure, and implications for resilience to perturbations. We are also more generally interested in extensions of this work to other
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effects in pediatric cancer. Collaborate with other researchers to better understand the complex interactions between health determinants and disease outcomes. Extract and analyze large datasets
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generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical
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with children or young people with lived experience and/or learning difficulties. Experience working with and synthesising large datasets. Demonstrated ability to undertake high quality academic research
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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are conducting several large randomized clinical trials, cohort studies, and surveillance programs, in India, Latin America, and the United States related to nutrition, infectious diseases, and maternal and child