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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 23 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