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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
Similar Jobs About The University of North Carolina at Chapel Hill Carolina rates among the nation’s great institutions of higher education, set on an historic and beautiful campus that celebrates all four
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scholarship and outstanding education are interlinked and equally valued. We are seeking an experienced postdoctoral researcher for the research project DiAgnostic – Tracing Diatopic Variation in a Corpus of
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federation of 17 renowned higher education institutions. The federation brings together more than 250,000 learners and 50,000 staff in a collaborative community that is delivering high-quality research and
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funding is secured to extend employment. Monthly Salary: $5,186 - $5,862 | Commensurate with experience and education In accordance with RCW 49.58.110 , the above salary reflects the full salary range
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
recognized institution of higher learning, with no more than five years post receipt of this. For consideration, applicants need to submit in PDF format: Cover letter Curriculum vitae with full publication
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About us Biosciences is one of the world’s foremost centres for research and teaching in the biological sciences and one of the largest Divisions within UCL, undertaking a significant amount of
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Foundation). The DIAN-TU’s Clinical and Research Fellowship in clinical studies in autosomal dominant Alzheimer’s disease at WashU Medicine is designed to develop competent physicians and independent
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qualifications In accordance with RCW 49.58.110 , the above salary reflects the full salary range for this position. Individual placement within the range is based on the candidate’s current experience, education
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on cognitive diagnostic assessments and student learning in introductory STEM courses, with a focus on Bayesian quantitative methods and mixed-methods education research. Position Number 4C1852 Department
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develop and evaluate semi automated machine learning techniques for content analysis adapted to handwritten documents and early printed books. These approaches will build upon existing optical character