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Classification Title: FWS Student Assistant - Map & Imagery Library Classification Minimum Requirements: N/A Job Description: Student Assistant needed for the Map & Imagery Library at the University
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-time 3D mapping on compact, low-power devices. The project will combine optical sensing, event-based vision, and radio-frequency (RF) data with advanced AI to build robust mapping systems for challenging
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basic copy cataloging of maps and monographs. For more information about the Map Room, please visit our website at: https://library.tamu.edu/collections/maps/ Salary: $15.85/hourly Cover Letter/Resume: A
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disease to join our CDC-funded project team to strengthen partnerships with public health in the Ohio River Valley and develop predictive models and hazard maps that inform public health decision-making
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missions: resource mapping and in-situ utilization, planetary habitability, robotic mobility, astronaut health and protection, innovative habitat design, and bioregenerative life-support systems. It also
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will carry out an integrated mapping of relevant textual, audiovisual and media sources to achieve a diachronic reconstruction of the definitions, lexicalisation processes and semantic transformations
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technologies on financial markets, money, and financial assets, including the evolution of blockchain technology, digital assets, and cryptocurrencies. The project aims at mapping the functioning
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The project deals with the mapping of labour markets exposure
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 8 hours ago
Working knowledge of AutoCAD and CAD‑to‑GIS data workflows Experience with field data collection using GPS and Total Station equipment Proven experience with utility mapping and underground utility locating
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and international fees. Overview Interested in bioinformatics, and high-resolution mapping of human heart? This PhD project will investigate how computational geography methods can be scaled to analyse