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Field
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for the development of professional career skills, on-going technical and workplace learning, and an opportunity to apply subject matter expertise to new technologies, digital scholarship, creative writing, etc. All
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synaptic and cellular studies using patch-clamp electrophysiology, large-scale population recordings using 2-photon Ca2+ imaging in awake behaving mice, and advanced modeling + machine learning analyses
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medical treatment facility. One of the most exciting and unique components of DGMC is the Air Force Clinical Investigation Facility. At the CIF, you will have the opportunity to participate in cutting edge
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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. The research program embraces team science drawing from i.e., implementation science, data science, geospatial, epidemiological, and machine learning approaches to better understand broad contexts including
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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
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programming is a plus. Required Qualifications* Ph.D. degree in biomedical engineering, medical physics, electrical engineering, computer science, neuroscience, or equivalent disciplines. Good written and
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computer skills for accurate data collection using various hospital EMR systems (primarily EPIC systems) and willingness to learn various Data Capture systems (WebDCU, RAVE, Red-Cap) ability to work on site
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seminars Each Fellow receives $100,000, allocated as follows: $80,000/year for salary $20,000 for moving, immigration, research travel, and computer costs Five one-year fellowship positions are available
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on leveraging data from permanent forest plot datasets in the Caribbean to build a comprehensive view of tropical tree species in support of conservation and broader management actions. Learning Objectives