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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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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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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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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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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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population-level outcomes. Utilize advanced quantitative methods to analyze large healthcare datasets, including Medicare and Medicaid claims (MedPAR, Outpatient, Carrier, TAF). Develop reproducible code and
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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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for aging populations Why Join Our Fellowship? World-Class Mentorship: Receive primary mentoring from Dr. Periyakoil Access to a large network of research mentors across Stanford Medicine, School
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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
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/or experience with large-scale data analysis, algorithm development, or computational modeling. Required Qualifications: Doctoral degree in linguistics, cognitive science, psychology, hearing and