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external) is reimagining the experimental neuroscience pipeline with big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that
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disease, including AI-powered tools and new statistical techniques that leverage large datasets, heavy computational capabilities, and/or a robust understanding of biological systems to provide unique
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Science Lab is seeking a highly-motivated postdoctoral scholar to lead a large-scale, community-based, clinical trial of behavioral treatment for childhood obesity. The Solutions Science Lab designs
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?: No. The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $76,383. Big Data
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Postdoctoral Research Fellow with expertise in large language models (LLMs) and electronic phenotyping to join our dynamic team focused on advancing cancer research through innovative data-driven approaches in
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
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experience working with large, complex data from varied sources. The candidate should have proficiency with statistical software, ideally Stata, strong written and interpersonal communication skills, and a
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primary research areas: 1) statistical inference in high-dimensional and large-scale testing scenarios; 2) the development of novel model architectures for large-scale proteomics data; and 3) causal
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, and dementia more generally. Data sources for our work in this area are large-scale electronic health record data, medical claims data, mortality registries, and epidemiological cohort studies
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spatial and temporal monitoring results in enormous volumes of data, necessitating the management of large datasets and communication of extensive (and expensive) data packets. These factors necessitate new