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challenges and needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated CPU cluster, several GPU servers, as well as access to the National
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Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi-scale coupled physics simulations in support of the missions and programs of the US
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA
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(Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities through the CASS network. NYUAD also has guaranteed observing time on the Green Bank
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will create a personalized training and development plan with the supervisor. Minimum Qualifications Currently has or is in the process of completing a PhD, MD/PhD, DPhil or equivalent terminal degree
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Job Status Full-time Is the job 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
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contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library. Qualifications
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Researcher (R2) Country Sweden Application Deadline 22 Dec 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be