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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
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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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. • 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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and/or cutting edges machine learning techniques to make foundational discoveries in reproductive medicine. The annual salary for this full-time position starts at $76,383, dependent upon skills and
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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
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connectivity and graph-theoretic analyses Familiarity with MR sequence programming (Siemens or GE platforms) Machine learning / AI applied to neuroimaging data EEG acquisition and analysis Use of neuroanatomical