75 parallel-computing-numerical-methods Postdoctoral positions at Stanford University
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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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disease. Key responsibilities include: (1) Conduct computational methods development and analysis for systems-level understanding of host-immune responses to diseases. (2) Collaborate effectively with a
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determined pending application date and program needs. Group or Departmental Website: http://flentje.stanford.edu (link is external) How to Submit Application Materials: Submit your application via this link
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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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be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single-cell RNA-seq, ADT-seq, ATAC-seq, DNA
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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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an interest in applying health services research and policy methods to real-world questions in acute and population health. Key Responsibilities Conduct and manage research projects focused
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position to join NIH-funded projects in collaboration with the U.S. Census Bureau's Enhancing Health Data (EHealth) Program that develop new integrated data to improve our understanding of the socio-economic
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and qualitative research methods. Disciplinary background is open within environmental governance, including but not limited to: political science, political ecology, sustainability science, human