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QUALIFICATIONS: PhD in computer science, electrical/biomedical engineering, statistics, applied mathematics, or a related field. Strong track record in machine learning/deep learning with imaging data
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Expertise in primary cell isolation and culture, FACS, confocal imaging, and mouse genetics is preferred Experience with transcriptomics and programming language suitable for computational analysis (e.g. R
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the Basic Science and Engineering (BASE) Initiative of the Children's Heart Center at Stanford University and the Department of Genetics to work on understanding mechanisms of pulmonary arterial hypertension
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experience in professional activities through programs such as the Stanford Benchside Ethics Consultation Service, a research ethics consultation program to assist life sciences researchers in the resolution