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working with large healthcare datasets (EHRs, claims, registries). Proficiency in R or Python. Strong quantitative skills and familiarity with advanced modeling techniques. Excellent written and verbal
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a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make
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, with a focus on combination therapies that account for cell-cell interactions. Experimental model systems will include cancer cell lines, organoid/assembloid models, and clinical tumor samples. Projects
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spectrometry and animal models would be valuable but is not essential. Required Application Materials: Candidates should submit their CV. They will be asked for 3 individuals who can serve as a reference
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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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learners, both ambitious and team players, have a strong research publication record, and high-level understanding of programming and computer modeling. This postdoc position offers a motivated researcher an
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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to evaluate and model the health consequences of emissions of NOx, benzene, CO, and additional gaseous pollutants from natural gas stoves and other appliances. The exposure measurements and modeling will build
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be made better. This is a role for a researcher excited to work with big, messy, real-world data, motivated not just by building models but by improving systems: how clinicians work together, how
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labs at Stanford to tackle emerging clinical questions in oncology, utilizing various AI methods, predictive modeling approaches, and large language models. Specific areas of interest include but are not