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of the post-doc is to study how innovations in AI, especially adaptation of Large Language Models (LLMs) architectures for time-series data, can be used in study of aging, health span, and longevity
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and AI-driven computational approaches to reconstruct and redefine tissue architecture in 3D space. The goals of these projects are to uncover the rules governing the spatiotemporal dynamics of tumor
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written
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Stanford’s Department of Computer Science and the Data Science team at Stanford Health Care. Our research spans both core methodological advancements (e.g., developing novel ML architectures and evaluation
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particular, the postdoc will focus on applying reinforcement learning to discover vulnerabilities and failure modes in software systems that support critical infrastructure, in particular AI-based decision
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skills in statistical software (e.g. R, Stata, Python) and working knowledge in SQL Excellent written and oral communication skills Strong record of distinguished scholarly achievement, including written
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and machine learning based software to assist clinical workflow and pre-clinical studies. Recent software developed from the group has been adopted in the clinic and preclinic labs. The scientific
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/ljiYqBbnJkOn3jp2EpXY6g/project-details/10720073#description (link is external) (4) Develop, deploy, and evaluate software systems and data analytics to improve inpatient hospital care value efficiency. For example
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excellent benefits and competitive salaries. Support is available for 1-year appointment with renewal up to 3 years of training and includes: Tuition, books, and software; Research-related costs; Conference
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred