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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours 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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language processing, historical linguistics, and computational humanities. The postholder will lead the computational stream of the project, which involves building a large corpus of Latin texts (data collection and
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expertise in Neuropixels or other large-scale in vivo electrophysiology techniques. An expert neural data analyst may be considered even with minimal animal experience. Required Application Materials: Cover
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equivalent to PhD level in health data science or similar field. Experience working within multidisciplinary teams, in medical statistics and in the analysis of large healthcare datasets (such as CPRD
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to analyze human gut microbiome data from large cohorts. Responsibilities Microbe-microbe and host microbe interaction analysis in multiple cohorts (n=2500) Evaluate differential fitness of specific strains in
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language processing, historical linguistics, and computational humanities. The postholder will lead the computational stream of the project, which involves building a large corpus of Latin texts (data collection and
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graduates of PhD programs in statistics, economics, computer science, operations research, or related data science fields. The position provides opportunities to participate in rigorous, quantitative research
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, collaborative research that combines lab-based experiments with large-scale data handling and analysis. The post is available for up to 25 months in the first instance with a preferred start date of July/ August
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submissions as required for ongoing and future studies. You will generate and analyze large data assets, adapt existing and develop new research methodologies and materials. You will report research findings in
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health