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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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learning to investigate how the human brain develops diverse cell types and forms complex neural circuits. We are particularly interested in how these developmental programs are disrupted in
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. Candidate will have the opportunity to investigate human Tregs in vitro and in vivo, learning from patient samples and humanized mouse models, implement state-of-the-art technologies such as functional
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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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. • Develop computational and theoretical models that bridge neural data and behaviour, leveraging modern machine‑learning toolkits. • Drive multi‑lab collaborations across SCENE; co‑author high‑impact
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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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-scale multimodal datasets and collaborating with leading experts in spatial biology, AI, and cancer research. Responsibilities: Design and train state-of-the-art generative AI architectures (e.g
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practice and policy to increase equity and opportunities for all students. The SCALE Initiative partners with school districts, service providers, and umbrella organizations across the country to learn about
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receptor (CAR) T-cell therapies for pediatric solid tumors. The Ramakrishna laboratory focuses on optimizing CAR T-cell therapies for children with cancer by learning about the biology of these CAR T-cells