13 process-optimization Postdoctoral positions at Oak Ridge National Laboratory in United States
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frameworks linking molecular interactions to cellular and network-level behavior (e.g. protein-protein interaction, PPI, network analysis) Optimize simulation codes and workflows for leadership-class HPC
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-intensive process because it is shown to be a leading driver of computational accuracy. As a result, for certain use cases of interest to ORNL, we are in need of an applications engineer capable of generating
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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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‑guided optimizations across languages (Julia/JACC, Mojo/MLIR, Rust/LLVM). Incorporate Enzyme-based automatic differentiation and multi-language IR tooling for AI‑driven analysis. High‑Productivity
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-class S&T products for sensitive national security missions. The selected candidate will support research efforts in signal processing and analysis, with an emphasis on the development of novel algorithms
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planning using operation model development, either through development of bespoke simulation/optimization tools, or through application of tools like RiverWare, WEAP, WRAP, StateMod, OASIS, WRIMS, and HEC
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seeking to fill a Postdoctoral Research Associate position to work in the areas of environmental life cycle assessment (LCA), technoeconomic optimization; and industrial energy efficiency and production
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optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy