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are a school for leaders not just in education, but also business, sport, health, and government. https://education.tamu.edu/about/ Dr. Erbeli leads a research program focused on reading development and
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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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23 Dec 2025 Job Information Organisation/Company Computer Vision Center Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country
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18 Feb 2026 Job Information Organisation/Company LIVERPOOL JOHN MOORES UNIVERSITY Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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in professional organizations Basic Qualifications: A PhD in Mathematics, Applied Mathematics, Computational Science, or a related field completed within the last 5 years Preferred Qualifications
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, and managing all professional and technical staff; and supervising students. Qualifications: Mandatory: PhD in Computer Science or similar field; At least one (1) publications as first/second author in
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craniofacial project and the application of dental data to the creation of a 3D digital avatar. You will require a 3D animation, computer science and/or anatomical modelling background. A knowledge of dental
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) Application Deadline 8 Mar 2026 - 22:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not
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academic areas. Learn more at https://www.uta.edu/administration/president/strategic-plan/rise100 . This is an exciting time to join UTA and contribute to its bold vision for the future. Working Title EEO
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 20 days ago
have a PhD, DrPH (or equivalent) in statistics, biostatistics, computational biology, or a related quantitative field. Required Qualifications, Competencies, and Experience Background in causal inference