60 optimization-nonlinear-functions Postdoctoral positions at Oak Ridge National Laboratory
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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of
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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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with world-class scientists, you will enhance your expertise in resource optimization, scalable computing techniques, fault resilience, and advanced AI applications. This role offers unparalleled access
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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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array of capabilities in nuclear nonproliferation, data analytics, cybersecurity, cyber-physical resiliency, geospatial science, and high-performance computing, our organization seeks to produce world
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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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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
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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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guarantees while minimizing performance impact. Additionally, you will optimize the balance between privacy and utility, addressing the challenges of heterogeneous privacy budgets and varying requirements