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reports Your Profile: Genuine interest in data science and one or more of its application domains: life and medical sciences, earth sciences, energy systems, or material sciences University degree (M.Sc
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science, and health-economic evaluation. We collaborate closely with other leading academic institutions (e.g., Harvard, UCSF, Johns Hopkins), global research networks (R2D2, Smart4TB), global health
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Posting Details Student Title Classification Information Quick Link https://chapman.peopleadmin.com/postings/38877 Job Number SE169624 Position Information Department or Unit Name Desktop Support
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EUMETSAT - European Organisation for the Exploitation of Meteorological Satellites | Darmstadt, Hessen | Germany | 1 day ago
17 Jan 2026 Job Information Organisation/Company EUMETSAT - European Organisation for the Exploitation of Meteorological Satellites Research Field Computer science Environmental science Physics
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such as Vectorworks & Lightwright. Knowledge of lighting control and networking technology, including an understanding and ability to troubleshoot traditional DMX networks as well as wireless and node-based
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position in the area of Security of Emerging Network and Networked Systems. SCAI (https://scai.engineering.asu.edu/ ), one of the eight Fulton Schools, houses a vibrant Computer Science Engineering research
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technology-related information, products, and support. HITS also provides institutional expertise related to IT strategy, security, infrastructure, and resource management. The Network and Communication
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an experienced BAS-focused engineering professional to provide engineering design, standards, and program oversight for campus Building Automation Systems that support learning, research, and campus life. In
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, including LAN/WAN (wired switches and routers), WLAN (wireless access points and controllers), network security (VPN, firewalls, and authentication systems), network support (DNS/DHCP, network management
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. This postdoctoral position is funded through Hi!PARIS Chair ATLAS (Advancing efficient, reliable and science-informed Learning for non-euclidean data with Application to molecule and biological network Structures