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Field
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Earth observation systems, enabling coordinated, event-driven operations across heterogeneous space assets through onboard intelligence, distributed computing and inter-satellite communications. By
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successful candidate will collaborate with scientists in DIS, Environmental Sciences division and Argonne leadership Computing Facility to further develop the AI foundation models, benchmarking and deploy them
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with a high pulse power device. Another study intends to develop non-thermal electrons distribution which is of trendemous importance for UHI experiments. Durée du contrat (en mois) 2 ans + 2 x 1an
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simulations of amorphous optical-coating materials using methods such as density-functional theory, ab initio and classical molecular dynamics, and related computational materials techniques 3. Analyzes and
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Application Deadline 31 Dec 2026 - 23:59 (Europe/Rome) Country Italy Type of Contract Temporary Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Horizon Europe Is the Job
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 7 days ago
6 Oct 2026 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile Recognised Researcher (R2
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field. The successful candidate must possess expertise in distributed AI/ML systems and computer networks. Good knowledge and practical skills in distributed AI/ML, networked systems, data management, and
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The postdoctoral researcher will
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scientific imaging. The primary responsibility of this position is designing and implementing sparse algorithms for large-scale scientific and numerical computations. The successful candidate will pursue
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extending to 2100 and, where appropriate, 2300; develop and/or integrate hierarchical species distribution models and/or dynamic vegetation modelling approaches, using contemporary and historical biodiversity