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common complex human genetic diseases using genomics and other omics data within large-scale health datasets. The postholder will contribute to objectives and development of a research programme funded by
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common complex human genetic diseases using genomics and other omics data within large-scale health datasets. The postholder will contribute to the objectives and development of a research programme funded
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computer scientists, experimentalists and clinicians. You will be involved in cross-disciplinary collaborations and have training opportunities to further develop and grow your scientific interests. Access
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 28 days ago
to contribute to innovative projects at the forefront of healthcare delivery improvement, leveraging Large Language Models (LLMs) and clinical data analysis. About the Position Our Postdoctoral Research Program
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healthy aging in families, and 2) the prediction of health outcomes in the general population using molecular, socioeconomic, and environmental data. You will work with very large, globally unique registry
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Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave Background (CMB) probes on very large
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; managing and analyzing large-scale databases, including neuroimaging data and cognitive assessments; good writing skills with a track record of published manuscripts; software skills for data visualization
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publications - Comprehensive skills in genetic and molecular biological techniques - Skills with high-throughput screening approaches - Experience with large-scale data analysis of high-throughput methods
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Alonso and others at the Beecroft Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave
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: Using big data insights to optimise the manufacturing process The second phase of this project will focus on processing and utilising machine-learning techniques to analyse large volumes of data from