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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
injections; histological and immunohistochemical analyses. Basic computer skills and use of Word, Excel, PowerPoint, Adobe Photoshop. Proficient use of the English language. Preferred Qualifications
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and quantitative gene expression analysis Excellent organizational, communication, and teamwork skills Certificates/Credentials/Licenses Computer Skills General office suite and willingness to learn lab
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and Experience PhD, MD, or equivalent doctoral degree in Neuroscience, Biomedical Engineering, Computer Science, or a related field. Candidates in ABD (all but degree) status will also be considered
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to efficiently collect/organize/analyze data and initiate new/modified procedures/techniques based on the latest developments are expected. Certificates/Credentials/Licenses n/a Computer Skills General office
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- Regular Salaried Payroll Designation PeopleSoft Seniority Unit Terms of Appointment Staff - 12 month Position Pension Eligibility ABP Qualifications Minimum Education and Experience A PhD degree in Computer
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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; keep a detailed notebook summarizing experiments and recording research data; maintain computer database of research data; tabulate and display data for presentation in research conferences and for
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. Preferred Education and Experience Preferred Experience Experience with Speech, EEG, and fMRI data collection and analysis as well as computer programming is preferred. Other Qualifications To the extent
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to the public and academic audiences. Anticipated Division of Time Lab Research 70%; Computer Research 10%; Field/Greenhouse Research 5%; Writing and manuscripts 10% Requirements PhD in Plant Biology, Entomology
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motivated and skilled postdoctoral researcher to lead projects related to PhacoTrainer—computer vision models for cataract surgical video recognition. Project themes will include validating PhacoTrainer