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, United States of America [map ] Appl Deadline: (posted 2025/09/04, listed until 2026/02/20) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological
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, United States of America [map ] Subject Areas: Machine Learning Computer Science Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Appl Deadline: none (posted 2025/08
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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the global health scenario and domestically for dissemination, and plenty of opportunities for career advancement. •Learn background/research methods of studies for which analysis is conducted with limited
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and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and publish high-quality
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Commitment. The program emphasizes faculty-mentored research, teaching innovation, and public engagement, providing Fellows with resources and networks to launch impactful careers. To learn more about the
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. The program emphasizes faculty-mentored research, teaching innovation, and public engagement, providing Fellows with resources and networks to launch impactful careers. To learn more about the SCALES
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. To learn more about the SCALES Postdoctoral Fellowship Program, visit our program page: https://climate.duke.edu/what-were-doing/scales-postdoctoral-fellows-program/ . Key Responsibilities Research
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engagement, providing Fellows with resources and networks to launch impactful careers. To learn more about the SCALES Postdoctoral Fellowship Program, visit our program page: https://climate.duke.edu/what-were