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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 Large
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how we decode cancer biology from spatial multi-omics data. This position offers an exceptional opportunity to build next-generation generative models for complex biological systems, working with large
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creation and iteration, data analysis, reporting, and more Knowledge of social platforms, and current and anticipated digital communication trends Experience with industry-standard tools used to conduct and
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employ advanced analytical methods in large databases, which include claims data and electronic health record data in conventional structures and in common data models. Our research group prioritizes a
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paradigms in primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science
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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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global supply chain logistics. While ML has the potential to transform both applied large-scale optimization and theoretical combinatorial optimization, algorithmic reasoning remains a significant
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into tangible products. Critically, this work will generate a large open-source dataset of child-created games that can inform future designs of educational games and AI algorithms. The postdoctoral fellow will
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expertise in large language models (LLMs) and electronic phenotyping to join our dynamic team focused on advancing cancer research through innovative data-driven approaches in the Cancer Data Science Core
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urgent questions of practical relevance and to design studies to test pragmatic solutions, analyze data, and disseminate findings and implications. The program will also provide fellows with training in