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-cloud robotic control at our Garmisch-Partenkirchen campus. The project investigates how advanced robotic control and AI-assisted teleoperation can be safely deployed over communication networks with
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experience in cloud computing and utilizing data from the All of Us Research Program are particularly well-suited for this position. Outstanding U of A benefits include health, dental, vision, and life
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4 Sep 2026 Job Information Organisation/Company MUNSTER TECHNOLOGICAL UNIVERSITY Department Human Resources Research Field Computer science Researcher Profile Recognised Researcher (R2) Positions
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Apply now Job no:535376 Work type:Faculty - Pro Tempore Location:Eugene, OR Categories:Instruction, Computer and Information Science, Data Science Department: CAS CIS Computer & Information
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productive scientific results Preferred Qualifications Equipment Utilized High-performance computing; cloud computing; local workstations. Physical Demands and Work Environment Work will be performed in
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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5 Sep 2026 Job Information Organisation/Company Ghent University Research Field Computer science » Other Philosophy » Epistemology Philosophy » Ethics Philosophy » Philosophy of science Philosophy
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Pharmacology T32 Postdoctoral Training Program Vacancy ID PDS004510 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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. Experience with high-performance computing, cloud computing, GPU acceleration, or distributed data processing. Experience participating in international scientific collaborations and/or multi-institutional
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. In the ELUD research project, we address the question of if and when learning agents converge to an efficient equilibrium and when this is not the case. ELUD will design new algorithms for computing