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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 17 minutes ago
approaches include MHD, Vlasov, and hybrid codes run on computers ranging from PCs to massively parallel computers, while analytical work involves linear and nonlinear instability theories. Location: Goddard
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16 Jan 2026 - 23:59 (UTC) Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral
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algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational
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accelerators Hardware–software co-design for AI and transformer-based models. Responsibilities Conduct cutting-edge research in parallel computing and AI accelerators Develop and lead externally funded projects
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fusion (lidar, camera, IMU), embedded programming, and/or real-time computing? Are you ready to push the boundaries of autonomous navigation in challenging environments like forests and off-road terrains
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), embedded programming, and/or real-time computing? Are you ready to push the boundaries of autonomous navigation in challenging environments like forests and off-road terrains? Join our innovative project
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As part of the “VERTIGO” electronics PEPR, which brings
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing