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of scientific colleagues and students for collaboration. Required Qualifications: A PhD in one of the following: Epidemiology, Biomedical Informatics, Biostatistics, Public Health, Computer Science
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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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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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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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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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and non-scientific audiences. Contribute to writing research papers and grant proposals. Train and mentor students and research assistants as needed. Required Qualifications: PhD in Neuroscience
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of natural and built urban systems and human-system collaborations. Successful candidates will be expected to define an independent research agenda and to work closely with Stanford University faculty, PhD
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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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for this position include a PhD in Computer Science, Artificial Intelligence, Natural Language Processing, Human-Computer Interaction, or a closely related field. Candidates should have demonstrated expertise in