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Field
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the team You will join the Thermal NDE Research Team within the Department of Computer and Information Sciences. The group hosts state‑of‑the‑art IR cameras, induction coils and GPU‑accelerated
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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and work together to train models, architect systems, and run trading strategies. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster
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molecules [doi.org/10.1021/jacs.2c07572 , doi.org/10.26434/chemrxiv-2023-5kl9x ]. (iii) Developing GPU-accelerated multireference methods to improve the accuracy and robustness of current state-of-the-art
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numerical solvers for 2D and 3D phase field models Develop HPC-ready simulation pipelines for large-scale rupture and fracture-fluid systems Optimize performance for modern architectures including GPUs and
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
NASA's Jet Propulsion Laboratory, focused on developing a next-generation, GPU-based climate model that learns physics from data to improve the accuracy of its projections. Will collaborate with
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Jet Propulsion Laboratory, focused on developing a next-generation, GPU-based climate model that learns physics from data to improve the accuracy of its projections. Will collaborate with oceanographers
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reproducible research practices Desirable criteria Experience working with generative models or large language models Experience with large scale GPU-based model training and cloud computing Knowledge
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RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent written and oral communication skills. Motivated self-starter with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
. The postdoctoral scholar will be expected to improve on existing GPU-accelerated ocean models and develop laboratory experiments (in the Joint Fluids Lab at UNC), analyze results, publish in peer-reviewed journals