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equipment; typing, data entry; computer operation; use of computer software and other programs applicable to the assigned department*. Ability to: develop and teach technology related programs; consult with
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in Singapore alongside DSO National Laboratories, Defence Science and Technology Agency (DSTA), Temasek Laboratories @ NUS and @SUTD and the local defence industry. For more details, please view https
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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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5 Jan 2026 Job Information Organisation/Company Military University of Technology - Wojskowa Akademia Techniczna Department Faculty of Security, Logistics and Management Research Field Other
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5 Jan 2026 Job Information Organisation/Company Military University of Technology - Wojskowa Akademia Techniczna Department Faculty of Security, Logistics and Management Research Field Other
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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of Georgia leadership institution and is The Military College of Georgia. More details on the UNG Mission, Values, Vision, and Culture can be found at https://ung.edu/about/mission-vision-values.php
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | about 24 hours ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search
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of Expression and Academic Freedom found on-line at https://www.usg.edu/policymanual/section6/C2653 . The University of North Georgia, a regional multi-campus institution and premier senior military college
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data