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breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation. Major Duties/Responsibilities: Develop AI-ready datasets, digital twins, predictive
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execution environments on both CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure
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Python and scientific software development. Knowledge of fundamental machine learning concepts, including the training, validation, and application of neural network models. Experience with Linux-based
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, heat transfer) and interact closely with industry partners. These engagements will play a vital role in ensuring success of programs and the adoption by project sponsors, and in developing your network
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complex challenges in science, energy, national security, and health. Our researchers develop scalable machine-learning models, deep-learning architectures, and data-driven methods for platforms ranging
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defects that affect the performance of next-generation semiconductor devices. The candidate will collaborate with researchers across CNMS, ORNL, and the broader NSRC network working in materials
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). The postdoc will perform enhanced sampling molecular dynamics simulations using neural network potentials and deploy statistical mechanics (e.g., rare-event theory), Markov-state models for molecular
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three broad pillars: sparse and structured matrix computations, sparse tensor problems, and sparse network problems, as well as their interconnections. These efforts will yield significant theoretical
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tensor network algorithm development and application. Competency with common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel
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transfer) and interact closely with industry partners. These engagements will play a vital role in ensuring success of programs and the adoption by project sponsors, and in developing your network