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Organization U.S. Department of Agriculture (USDA) Reference Code USDA-ARS-SEA-2025-0182 How to Apply To submit your application, scroll to the bottom of this opportunity and click APPLY. A complete
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investigate development of functional covalent adaptable networks. Recently we have developed different types of inherently flame retardant dynamic networks for fire safe fiber reinforced composites and self
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 24 days ago
, Inserm and CRNS, which gathers an international team of researchers, engineers, clinicians and students interested in studying brain networks, to shed light on information processing, its modulation by
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-wave). Ability to perform advanced complex network analysis (preferably with experience on NetworkX). Ability to obtain advanced graphics (e.g. seaborn). Code in Python, C and BASH. Why Texas A&M
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/business network formation in low-income countries (possibly, in conflict affected areas, CAF). We seek a candidate with expertise in both empirical and theoretical economics and strong coding skills
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environments (e.g., Mininet, NS-3), MANO platforms (e.g. OSM) Solid understanding of 5G/6G network architectures and technologies is considered a strong asset Proven software engineering and coding skills (e.g
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for metal organometallic networks”, project number PN-IV-P2-2.1-TE-2023-0141. -Part time position in experimental chemistry (synthesis and characterization of organometallic compounds) Where to apply E-mail
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 17 days ago
Job Code: 07/2025 Job Offer from November 21, 2025 We are seeking to fill one postdoctoral position as part of the Simons Collaboration on Learning the Universe . In this international network
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future. The opportunity for involvement with the School’s Innovation Research Networks (IRNs), which are at the heart of the School’s research mission, is a key unique feature of this position. The primary
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the adaptability, efficiency, robustness, and scalability of next-generation networks. While deep neural networks tailored for specific tasks have shown strong performance, advances in fields such as natural