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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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fellowships and NSF SBE postdoctoral awards. We especially welcome applicants with theoretical interest in child language development, strong computational and analytical skills (deep learning frameworks), and
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, and MRV performance) and identify optimal deployment models coupled with learnings from forest management. Conduct techno-economic and life-cycle assessments (TEA/LCA) integrating forest operations
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Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $76,383. Deep Phenotyping of Learning Differences The high-level
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. Required Qualifications: PhD in Computer Science, AI/ML, Computational Biology, or a related quantitative field. Proven expertise in deep generative modeling and large-scale multimodal learning. Experience