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models struggle to quantify uncertainty, risking overconfidence in volatile scenarios. The goal is to scale and enhance Bayesian Distribution System State Estimation using both simulated and, if necessary
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the fields of visual processing and spatial navigation. Applicants should have completed a PhD in Computer Science and have extensive experience in distributed systems and event-driven architectures. We
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) calendar days. If you have any questions, please visit our Careers site at https://careers.utrgv.edu for detailed contact information. Additional Information UTRGV is a distributed location institution and
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research group of IDLAB is seeking a highly motivated and talented PhD student to join its team. DECIDE conducts research on Distributed and Embodied Computing in Dynamic Environments. Our research spans
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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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future for all. Connections working at Michigan State University More Jobs from This Employer https://main.hercjobs.org/jobs/22004174/maintenance-mechanic-distribution-mechanic-maintenance Return to Search
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Offer Starting Date 2 Mar 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 30267440009 Is the Job related to staff position within a
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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reporting of outreach laboratory specimens directly impacting patient care: specimen handling, proper specimen storage, test ordering, test availability, specimen requirements and distribution of patient
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 22 days ago
(supercomputer/cloud/edge) infrastructures. Developed by the KerData research team in the context of the STEEL project of the national PEPR CLOUD program, E2Clab (https://team.inria.fr/kerdata/e2clab/ ) is a