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: The Pain Intelligence Lab The Stanford Center for Population Health Sciences Interdisciplinary collaborations within Stanford School of Medicine Opportunities to engage with national networks in rheumatology
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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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algorithmic performance. For instance, the scheduling problems that an electric grid operator faces will change daily, but not drastically: although demand will vary, the network structure will remain largely
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ability in a diverse sample of children with dyslexia and typical readers. Scientific reproducibility and transparency: Our team has a long history of developing open-source software to support rigorous
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field centered on the material culture of the ancient Mediterranean world, broadly defined, including those whose research situates the Mediterranean in dialog with other regions and periods. The ideal
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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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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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. 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
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spatial genomics experiments is a plus but not necessary. Experience in carrying out independent and collaborative research. Proven experience with protocol development, software development, computational
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uniquely cross‑disciplinary team and work closely a network of collaborators. • Jan Zimmermann — University of Minnesota • Aaron Batista — University of Pittsburgh • Kimberly Stachenfeld — Columbia