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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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research spanning detector simulation, Spiking Neural Network (SNN) design, neuromorphic hardware, and data-rich experimental systems such as CMS pixel detectors, Timepix4, and novel photodetector
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networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The position is supported by
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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 platform management/partitioning
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environment. They are supported with mentorship, conference travel, and opportunities to develop independent research directions and build professional networks. Oak Ridge and the greater Knoxville area offer
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on circular economy research Experience in working in the genetic algorithm and artificial neural networks is preferred. Experience in manufacturing process modeling of advanced manufacturing technologies
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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from plant genomics to phenomics with biological mechanisms embedded in deep neutral networks. GPTgp will allow task-specific training and transfer learning across reactions, pathways, biodesigns, and