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at conferences and publish results in peer-reviewed journals. Support mentorship of junior researchers and/or students. Required Qualifications: PhD in Computational Organic Chemistry or Computational Materials
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. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will involve both method
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faculty, PhD students and researchers. The ideal candidate will have earned a Ph.D. in applied science and engineering discipline, with demonstrated expertise in a complementary area (e.g., a Ph.D. in
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based on Stanford University guidelines , and full benefits. Required Qualifications: Recently completed PhD, DrPH, MD, or other doctoral degree in a discipline related to nutrition, food systems
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research expenses. Please direct queries to Professor Lisa Surwillo (surwillo@stanford.edu (link sends e-mail) ). Required Qualifications: Applicants must have received a PhD from an accredited university in
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a PhD in the field of molecular and cellular cancer biology, relevant publications, curiosity for science and innovative thinking, and high fluency in English. Experience with mammalian cell culture
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presentations). Evidence of their contributions to their current research communities. Track record of mentoring more junior scholars. Required Qualifications: PhD in computer science, electrical engineering
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(PhD, MD, or equivalent) conferred by the start date. Proven research and/or professional experience in machine learning and/or natural language processing, with a preference for prior experience working
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, biostatistics, and related disciplines who meet the required qualifications are encouraged to apply. Required Qualifications: Doctoral Degree (PhD, MD, or equivalent) conferred by start date. Demonstrated
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cancer epidemiology with a Ph.D. degree in biostatistics, operation research, epidemiology (with strong computation skills), or related fields and hands-on experience in algorithmic implementation and