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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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for highly motivated postdoctoral candidates with a PhD in bioengineering deep knowledge in computational biology and machine learning. Candidates with a molecular biology or engineering degrees with
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with neuroimaging and neural signal processing tools, including fMRI, structural MRI, diffusion MRI, EEG, or related modalities. Strong publication record in AI, machine learning, computational
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graduate-level knowledge of spatial analysis, machine learning, large dataset management, and statistics or econometrics. Applicants must be proficient in R and/or Python. Advanced knowledge of R's and/or
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demonstrated aptitude to quickly learn pharmacometrics. Comprehensive understanding of scientific principles and expert-level knowledge in fields related to the research project. Proficiency in data analysis and
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field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
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appointed through the Office of Postdoctoral Affairs. The FY27 minimum is $79, 056. Postdoc in Children's Language and Learning in Everyday Environments Advisors: Monica Ellwood Lowe, Ph.D., Meg Cychosz, Ph.D
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Department with significant expertise in Machine Learning/AI are available to help provide overall guidance and strategy to the postdoc. Stanford Departments and Centers: Anesthesiology, Perioperative and Pain
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a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make
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and experimentalists working across species as part of SCENE The Tolias Lab fuses large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly