74 parallel-computing-numerical-methods Postdoctoral positions at Stanford University
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PhD graduates who are passionate about leveraging computational methods to transform trauma and acute care surgery. Fellows will work at the intersection of clinical medicine, data engineering, and
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training and skills development — across a wide range of experimental and computational techniques. And through collaboration, strong work ethic, seeking feedback, and trying out new strategies, we drive
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analysis of multiple disease-specific datasets and contribute to the development of novel methodologies in this space. The ideal applicant will have a strong background in bioinformatics methods and a keen
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science, computer science, health or environmental sciences, or environmental economics Experience with causal inference methods, especially fixed-effects regression A demonstrated interest in
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therapeutics. We are seeking a highly motivated, collaborative, and independent Postdoctoral Researcher to spearhead a research program within the general areas of protein biochemistry, engineering, and
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are seeking a highly motivated, collaborative, and independent Postdoctoral Researcher to spearhead a research program within the general areas of synthetic genomics and synthetic biology, as
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and policy methods to real-world questions in acute and population health. Key Responsibilities Conduct and manage research projects focused on emergency medicine health services, care transitions, and
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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single
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. The postdoctoral scholar will join a research team focused on: (1) using novel methods to create metrics based on task interaction data and verbal responses from children in pre-K through grade 2; (2) validating
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responsibilities include: (1) Conduct computational methods development and analysis for systems-level understanding of host-immune responses to diseases. (2) Collaborate effectively with a team of multidisciplinary