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ability to quickly learn and master various computer programs. Strong record of peer-reviewed publications. A PhD, MD or equivalent with prior relevant training in Immunology, Biology, Bioinformatics
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demonstrated ability to design, train, and deploy large-scale models Expertise in at least one of computer vision, speech recognition, or multimodal learning, with experience in real-world technology deployment
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deep learning algorithm development, remote sensing, geospatial analysis. We welcome applicants from ecology, environmental science, climatology, geography, geophysics, computer science, or related
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partners in genomics, cancer biology, and clinical research, providing opportunities to work with diverse biological systems and translational applications. Required Qualifications: PhD in bioengineering
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and/or related fields including genetic epidemiology, human genetics, biostatistics, bioinformatics, computational biology, or phenomics (including EHR, deep medical imaging, and biomarker profiling
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sends e-mail) ) Required Qualifications: PhD completed by the appointment start date in computer science, data science, human-computer interaction, psychology, sociology, political science, communication
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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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Centers: Pediatrics, Cardiology Genetics Postdoc Appointment Term: Initial 2 years then reappointment possible Appointment Start Date: Immediately Group or Departmental Website: https://med.stanford.edu
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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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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