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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen
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on hotspot detection. This modeling work is well supported by large-scale primary datasets, including survey-based, parasitological, serologic, and genomic data. Relevant methodologies include mechanistic
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healthcare innovation, leveraging big data to improve patient care and outcomes. The successful candidate will focus on designing, training, and implementing novel deep learning models, particularly large
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/summerhanlab.html (link is external) How to Submit Application Materials: Please email a cover letter, CV, a short description of research interests, and contact information of three referees to: Summer Han, Ph.D
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Application Materials: Please send your application package as a zipped file to kseetah@stanford.edu (link sends e-mail) , with the subject line: Application for 'Integrating Natural and Cultural Data' postdoc
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and analysis. Experience managing large datasets and executing data analysis in complex environments is highly valued. Required Application Materials: Application Instructions To apply, include
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that promote high quality healthcare. Our projects apply advanced analytical methods to large databases of primarily structured electronic health record data and EHR usage metadata. The position will have the
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: ● Extensive tissue culture laboratory experience (at least 1 year) is required. Good laboratory practices and sterile culture techniques are critical. ● Ability to undertake and complete large research projects
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) access have been shifting since 2020, and how state-level child care assistance policies affected ECE access. Research activities of this project include secondary data analyses and manuscript development
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help psychometrics become a more data-driven discipline. Our labs are combining in this project to propel discovery about development of children’s cognitive abilities in heterogeneous contexts