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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
. Opportunities may also exist to participate in planned field campaigns in Greenland. The postdoctoral scholar will be expected to improve on existing GPU-accelerated ocean models and develop laboratory
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. Knowledge and Professional Experience: DFT-based methods. Scientific programming in Fortran, in MPI/OpenMP-parallelised codes. Knowledge of other languages (in particular python) and of GPU offloading will be
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the computer science research conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings
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other scalable approximations. Experience running large-scale computations on supercomputers (e.g., SLURM job scripts, GPU acceleration, MPI communication). Notes: This is a full-time, 2 year
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approximations. Experience running large-scale computations on supercomputers (e.g., SLURM job scripts, GPU acceleration, MPI communication). Notes: This is a full-time, 2 year, postdoctoral appointment with
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-getalp.imag.fr/ ), which offers a dynamic, international, and stimulating environment to conduct high-level multidisciplinary research. The person will have access to large datasets, GPU servers, means
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projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities
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framework and implemented on our research scanners for pre-clinical trials and validation at the University of Copenhagen using GPU processing. Qualifications Candidates should have a PhD degree in electrical
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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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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