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Field
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AI-driven network functions). Develop and integrate 6GApp‑like components, including: vertical application logic that reacts to network conditions, network‑application logic (AI‑based optimization
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applications. A reverse modeling strategy will be used to design an optimum test matrix. This framework will enable modeling and predicting ion-irradiated mechanical properties using reduced test data, thereby
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system perturbations and how they evolve, dilute, and dissipate in dynamic coastal environments. Although the primary observational tool will be atmospheric Eddy Covariance, the researcher will be embedded
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foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable
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modeling. Positions may begin as soon as September 2025 . A Ph.D. in Mechanical and Aerospace Engineering, Civil and Environmental Engineering, Atmospheric and Oceanic Sciences, Geosciences, Computational
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 7 hours ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). Biomass burning is an important source of aerosols and trace gases to the atmosphere and is a major
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focus on Computer Vision, pattern recognition, and visual generative modeling. CVI2 offers the opportunity to contribute to a diverse portfolio of projects, including collaborations with industrial
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The Monroe group at the Oxford University Department of Engineering Science seeks a full-time Postdoctoral Research Associate. This post is funded by the Faraday Institution Multiscale Modelling
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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LIST? Check our website: https://www.list.lu/ How will you contribute? The Post-Doc researcher will develop, implement, and apply advanced ways in inverting a radiative transfer model for forest trait