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, mathematics or related majors Job Description: The Postdoctoral Researcher will play a key role in developing and applying cutting-edge AI methods to analyze large-scale clinical datasets, including electronic
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implementation of these within optimized computer code, but also large-scale applications of the resulting methods to various chemical problems of interest. Candidates with a strong background in theoretical and
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with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will also contribute to the development of interactive tools and training materials
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? In addition to salary, our Total Rewards benefits and Compensation Estimator give you a clear view of the complete package. Context of Job: The Post Doctoral Researcher will work with the Center
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
47,476 - 68,000 Proposed Start Date 11/10/2025 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public
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software and high-performance computing (HPC). These include particle and gravitational physics. -On the data analysis side, the group designs novel statistical methods for particle physics and astrophysics
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challenging task. In most cases, only a sample of a network is observed. Therefore, network completion needs to be addressed. Matrix completion methods have proved to be efficient when reconstructing a non
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to join our team. Our lab focuses on developing and applying innovative statistical machine learning methods, single-cell multi-omics, and systems immunology approaches to investigate immune-mediated
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and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will
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of psychiatric epidemiology and dimensional approaches to mental health measurement - Expertise in longitudinal data analysis and advanced statistical methods - Proficiency in statistical programming (R, SAS