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Field
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. Experience applying computer vision, image analysis, and/or machine-learning methods to microscopy or materials characterization data. Demonstrated ability to analyze microstructural data and relate
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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clinical or imaging-based research. Familiarity with vascular imaging software, image post-processing, and AI/machine learning applications in clinical or imaging research is desirable. Demonstrates strong
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. Familiarity with vascular imaging software, image post-processing, and AI/machine learning applications in clinical or imaging research is desirable. Baylor College of Medicine is an Equal Opportunity
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field. • Experience conducting medical imaging research. • Experience developing artificial intelligence and machine learning approaches for research. • Strong command of statistical methods and their
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following areas is desirable: collection and analysis of dense nodal seismic datasets, ambient noise tomography, high-performance computing, machine learning applications in observational seismology, and
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for immunological tolerance during autoimmunity and transplantation, directed at therapeutic intervention strategies. We are using autoimmune animal models to understand biological processes involved with autoimmune
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, Raspberry Pi, or similar platforms). • Experience with computer-aided design (CAD) software and rapid prototyping techniques, including 3D printing. • Experience developing artificial intelligence and machine
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. The Aleks Tata lab in the Department of Surgery, Duke University School of Medicine has an opening for postdoctoral researchers to study mechanisms in lung injury-repair. We seek to understand
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seeking applicants for a full-time postdoctoral scholar position in Computational Immunology and Genomics. The successful candidate will develop and apply computational, statistical, and machine-learning