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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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, 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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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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brain evolution. We employ a multifaceted strategy to bridge developmental neurobiology, RNA biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu
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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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, 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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innovation, and public 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