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, United States of America [map ] Appl Deadline: (posted 2025/09/04 05:00 AM, listed until 2026/02/21 04:59 AM) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning
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regional leadership in biostatistics, genomics, biomedical informatics, artificial intelligence and health data science. The Postdoctoral Associate will conduct research in statistical machine learning and
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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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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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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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, environment, or sustainability challenges. • Required skills: o Strong quantitative background, with expertise in one or more of the following: statistical modeling, machine learning, remote sensing
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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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, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control the dynamics of microbial communities in time and space. Ongoing projects
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Overview We are seeking a highly motivated postdoctoral researcher with expertise in artificial intelligence and machine learning (AI/ML) to join our interdisciplinary team at Duke University. The postdoc
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in