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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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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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infections using electronic phenotyping, supervised machine learning, live Epic/FHIR implementations for silent deployment, and multi-site data coordination. https://reporter.nih.gov/search
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
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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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learning experts will be an essential and enriching component of the position. Strong candidates will have a background in machine learning and natural language processing (NLP), with a demonstrated ability
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Postdoc Appointment Term: 2 years Appointment Start Date: 01-01-2026 Group or Departmental Website: http://aging.stanford.edu (link is external) http://geriatrics.stanford.edu (link is external) How